Methods and compositions for assessing patients with reproductive failure using immune cell-derived microrna

ABSTRACT

The invention is directed to methods and compositions for collecting immune cells, preferably peripheral blood mononuclear cells (PBMCs), before or after an intervention, extracting microRNA-comprising RNA from said cells, quantifying microRNAs within the extracted RNA, determining one or more microRNAs that display a bimodal response amongst a statistically sufficient number of patient samples. Patients are then preferably segregated into groups according to their response.

RELATED APPLICATIONS

This application relies on provisional application Ser. No. 61/456,063filed on Nov. 1, 2010.

FIELD OF THE PRESENT INVENTION

This disclosure generally relates to immunology and more specifically tomethods and compositions for characterizing individuals or groups ofindividuals using patterns of expression of one or more microRNAsequences.

BACKGROUND OF THE INVENTION

Infertility and pregnancy failure is a vexing problem faced by coupleswho want to raise families. Spontaneous abortion occurs in 15 percent ofpregnancies. Recurrent spontaneous abortion, defined as the loss of atleast three consecutive pregnancies under 24 weeks gestation, occurs in3-4 percent of women. In addition, very early losses may go unrecognizedin couples thought to be infertile where no diagnostic test hasconfirmed the existence of a transient pregnant state. Still other womensuffer from pregnancy complications such as pre-eclampsia, intrauterinegrowth retardation (IUGR), preterm labor, premature rupture of membranes(PROM) and stillbirth. Childhood conditions like asthma, autism,attention deficit hyperactivity disorder (ADHD), diabetes, schizophreniaand Tourette's syndrome have been associated with pregnancy relateddisorders and the late complications of pregnancy-related disordersaccording to recent literature.

A significant body of literature now exists associating a number ofthese conditions with immunologic dysfunction. Immunologic interventionhas been shown to improve reproductive success in women who haveimmunological conditions such as low levels of T regulatory cells,elevated natural killer cells and high TNF-alpha/11-10 cell T-helperratios as determined by in vitro assays. Further it has been shown thatimmunotherapeutic intervention is helpful in specific subgroups of thoseafflicted. For example, intravenous immunoglobulin (IVIG) has beenuseful to reduce natural killer cell numbers in a subset of women inwhom in vitro measurements of immune function are elevated. Thisimmunotherapeutic intervention has been shown to improve reproductivesuccess in these women. JAMA (1995) 273:1933-36 Hum Reprod (1998)13(9):2620-3, Am J Reprod Immunol (2002) 38(5):312-18, Ann NY Acad Sci(2005) 1051:743-78. In addition, heparin has been shown to reducemiscarriage rates in those with elevated antiphospholipid antibodies(Salmon J E, Girardi G. Antiphospholipid antibodies and pregnancy loss:an inflammatory disorder. Reprod Immunol. 2008 January; 77(1):51-6. Epub2007 Apr. 5 Review.) Many reproductive conditions that have previouslybeen considered to be “unexplained” may be immunologically caused.

Amongst immunologic cells including lymphocytes, monocytes (includingtheir derivatives macrophages and dendritic cells) various lymphoidsubpopulations, alternatively known as subsets, mediate and regulateimmunologic cytotoxicity. These cells may include NK cells, NKT cells,CD8 T cells, CD4 T cells, gamma delta T cells T regulatory cells andTh17 cells. It has been suggested that the activity of such subsets mayhave particular significance in defining populations of women sufferingfrom or at risk of suffering from reproductive failure who might benefitfrom immunotherapy. In addition, assays quantifying the numbers andactivity of such cells have been helpful in monitoring suchimmunotherapy. In the clinical setting, the immunologic cells citedabove are collected from the peripheral blood compartment and assessed.

For example, one area of clinical interest has been the T helperlymphocytes (CD3+/CD4+). These cells can be classified intosubpopulations according to cytokine profiles revealed following invitro stimulation as either T helper 1 (Th1) or T helper 2 (Th2) cells.T helper cells selectively secrete specific clusters of cytokines. Forexample, Th2 cells produce interleukins, IL-4, IL-5, IL-6, IL-9, IL-10and IL-13, that, in turn, are involved in the development of humoralimmunity against extracellular pathogens but inhibit several functionsof phagocytic cells. Th1 cells, alternatively, produce interferon-gamma(IFN-gamma.), IL-2 and tumor necrosis factor-alpha (TNF-alpha). Thesecytokines are involved cell-mediated immunity and phagocyte dependentinflammation (Mosmann & Coffman, 1989; Romagnani, 2000).

Another area of clinical interest has been complement-mediated pregnancyloss (Cohen D, Buurma A, Goemaere N N, Girardi G, le Cessie S, ScherjonS, Bloemenkamp K W, de Heer E, Bruijn J A, Bajema I M. Classicalcomplement activation as a footprint for murine and humanantiphospholipid antibody-induced fetal loss. (J. Pathol. 2011 Mar. 10.)(Yu G, Sun Y, Foerster K, Manuel J, Molina H, Levy G A, Gorczynski R M,Clark D A. LPS-induced murine abortions require C5 but not C3, and areprevented by upregulating expression of the CD200 tolerance signalingmolecule. Am J Reprod Immunol. 2008 August; 60(2):135-40). Timelyintervention with anticoagulants prevent many of these losses.

Raghupathy observed significantly higher serum levels of Th2 cytokines,IL-6 and IL-10, in normal pregnancy compared to unexplained recurrentpregnancy losses. Further, significantly higher serum levels of the Th1cytokine, IFN-gamma, were present in women with recurrent pregnancylosses compared to normal pregnancy (Raghupathy et al., 1999). Together,these observations suggest a Th2 bias in women with normal pregnancieswhile a Th1 bias exists in many women with a history of recurrentpregnancy loss, unexplained infertility and pregnancy complications as,for example, delineated above.

Alan Beer reasoned that rebalancing the Th1/Th2 ratio of women with aTh1 bias toward a Th2 bias might help them achieve greater reproductivesuccess. He proposed that treatment of such women with anti-TNF alphaagents would result in shifting the Th1/Th2 balance toward a Th2 bias.

To identify candidates for anti-TNF alpha therapy, Beer proposed that invitro assessment of the proportions of CD4 expressing T cellsdifferentiated toward producing cytokines of either the Th1 or Th2groupings could be conducted. If a patient with a reproductive disorder,in particular the issues of unexplained infertility and recurrentunexplained abortion, demonstrated an elevation in the Th1/Th2 ratiocompared with normal control patients, then the patient is considered acandidate for anti-TNF alpha therapy. Beer proposed that patients undertreatment undergo repeated assessments of the Th1/Th2 ratio to assessefficacy of the therapy. Effective therapy, he reasoned, should resultin a shift in the Th1/Th2 ratio toward a Th2 bias. Winger, Reed et al.have shown that women with preconception Th1/Th2 ratio elevation doindeed demonstrate improved reproductive success when treated withanti-TNF alpha therapy over similar women who do not undergo suchtherapy. Moreover, the treatment period was found to be effective evenwhen the treatment period concluded prior to implantation. (Reference 1:Edward E. Winger, Jane L. Reed, Treatment with Tumor Necrosis FactorInhibitors and Intravenous Immunoglobulin Improves Live Birth Rates inWomen with Recurrent Spontaneous Abortion, 60(1), 8-16, PublishedOnline: 28 Jun. 2008. Reference 2: Edward E. Winger, Jane L. Reed,Sherif Ashoush, Sapna Ahuja, Tarek El-Toukhy, Mohamed Taranissi,Treatment with Adalimumab (Humira and Intravenous ImmunoglobulinImproves Pregnancy Rates in Women Undergoing IVF, American Journal ofReproductive Immunology 61 (2009) 113-120)).

The method of Beer has received significant criticism. One suchcriticism is articulated by Chaouat who notes that implantation of theembryo into the uterine lining is an inflammatory event. (GerardChaouat, Natalie Ledée-Bataille, Sylvie Dubanchet, Sandrine Zourbas,Olivier Sandra, Jacques Marta, Th1/Th2 Paradigm in Pregnancy: ParadigmLost?, Cytokines in Pregnancy/Early Abortion: Reexamining the Th1/Th2Paradigm, Int Arch Allergy Immunol 2004; 134:93-119) Predominance of Th1cytokines at the time and place of implantation is essential to theprocess thereby identifying a deficiency in the Th1/Th2 hypothesis.Moreover, anti-TNF alpha therapy, as suggested in the patent applicationof Kwak Kim et al. (USPTO #20040105858) might result in diminishedreproductive efficiency. Moreover, determination of the Th1/Th2 ratio atthe site of implantation may not be effectively determined from theanalysis of lymphocytes isolated from the peripheral blood. Anothercriticism that has been leveled at attempts to assay peripheral bloodcells has been made by Moffett et al. They challenge the examination ofperipheral blood white cells as non-representative of cellular eventswithin the placenta. (Ashley Moffett, Lesley Regan, Peter Braude,Natural killer cells, miscarriage, and infertility, BMJ 2004;329:1283-5.) They conclude 1) “Uterine NK cells are different from thosecirculating in peripheral blood”, 2) “Tests to measure NK cells inperipheral blood give no useful information on uterine NK cells” and 3)“Enthusiasm for new treatments aimed at natural killer cells in womenwith reproductive failure is unfortunately not backed up by thescience”.)

Anticoagulant therapy for antiphospholipid antibody related recurrentmiscarriage has also received criticism. Antiphospholipid antibody testshave been criticized as unreliable and poorly standardized. (Lakos G,Favaloro E J, Harris E N, Meroni P L, Tincani A, Wong R C, Pierangeli SS. International consensus guidelines on anticardiolipin and anti-â(2)glycoproteinI testing: A report from the APL task force at the 13(th)international congress on antiphospholipid antibodies. Arthritis Rheum.2011 Sep. 27. doi:10.1002/art.33349. Review.) In addition, it has beensuggested that miscarriages associated with Antiphospholipid AntibodySyndrome are actually caused by increased complement activity ratherthan increased thrombotic activity (Salmon J E, Girardi G. Theodore E.Woodward Award: antiphospholipid syndrome revisited: a disorderinitiated by inflammation. Trans Am Clin Climatol Assoc. 2007;118:99-114.) (Lynch A M, Salmon J E. Dysregulated complement activationas a common pathway of injury in preeclampsia and other pregnancycomplications. Placenta. 2010 July; 31(7):561-7. Epub 2010 Apr. 27.Review.) MicroRNA markers may better identify the underlyinginflammatory markers that respond well to anticoagulant treatment.

In addition, mechanisms of maternal tolerance of the fetal hemiallografthave invoked the interaction of another group of CD4 T cells known as Tregulatory cells. Jasper et al. (Molecular Human Reproduction Vol. 12,No. 5 pp. 301-308, 2006) quantified FoxP3 mRNA, a master regulator of Tregulatory cell differentiation, in mid-secretory endometrial tissuesfrom women with unexplained infertility and levels from unaffectedwomen. They found reduced levels of FoxP3 mRNA in affected women whencompared with controls. Winger and Reed have found diminished levels ofCD4+ CD25+ Foxp3+ T regulatory cells in the peripheral blood lymphocytesof women experiencing recurrent abortion (Edward E. Winger, Jane L.Reed, Low Circulating CD4+ CD25+ Foxp3+ T Regulatory Cell Levels PredictMiscarriage Risk in Newly Pregnant Women with a History of Failure, Am JReprod Immunol. 2011 October; 66(4):320-8).

Th17 cells are now thought to play a role in the immunology of pregnancy(Shigeru Saito, Akitoshi Nakashima, Tomoko Shima, Mika Ito, Th1/Th2/Th17and Regulatory T-Cell Paradigm in Pregnancy, American Journal ofReproductive Immunology 63 (2010) 601-610). Together with T regulatorycells, with which they appear to act in concert, they affect the balancein much the same manner as Th1 and Th2 cells have been proposed toaffect pregnancy success.

Recently, a variety of conditions affecting infants born wherein themother demonstrated immunologic abnormalities in or about the time ofpregnancy have been described. For example, immunologic abnormalitiesduring the course of pregnancy have been implicated a cause of autism.Autism is thought to be a spectrum of disorders the etiology of whichremains unknown. However, immunological factors during early pregnancyhave been invoked. Skewed Th1/Th2 cytokine profiles, altered lymphocytenumbers and decreased T cell mitogen responses have been identified inaffected children. The authors suggest that immune abnormalities duringearly pregnancy may be involved (Paul Ashwood, Sharifia Wills, and JudyVan de Water, The immune response in autism: a new frontier for autismresearch, Journal of Leukocyte Biology. 2006; 80:1-15)

Controversy regarding the Th1/Th2 hypothesis is also related to timingof testing and therapy. Chaouat, as noted above, teaches that thehypothesis is flawed and Th bias may not remain constant through theimplantation period. Winger, Reed et al. show that patients withelevation in their Th1/Th2 cell ratio assessed in the pre-conceptionperiod enjoy significantly superior pregnancy results when treated withanti-TNF alpha therapy. (Winger E E, Reed J L, Ashoush S, El-Toukhy T,Ahuja S, Taranissi M. Degree of TNF-α/IL-10 cytokine elevationcorrelates with IVF success rates in women undergoing treatment withAdalimumab (Humira) and IVIG. Am J Reprod Immunol. 2011 June;65(6):610-8) Winger and Reed have also demonstrated that T regulatorycells assessed during the period of implantation predict pregnancyoutcome as well. (Winger E E, Reed J L. Low Circulating CD4(+) CD25(+)Foxp3(+) T Regulatory Cell Levels Predict Miscarriage Risk in NewlyPregnant Women with a History of Failure. Am J Reprod Immunol. 2011October; 66(4):320-8.) Timing and the nature of the laboratoryparameters used appears quite significant.

In addition, Winger and Reed have identified a subset of patients inwhom the behavior of the ratio was not as expected. Occasionally Th1/Th2ratios rise following therapy while efficacy of therapy appears intact.(Winger E E, Reed J L, Ashoush S, El-Toukhy T, Ahuja S, Taranissi M.Degree of TNF-α/IL-10 cytokine elevation correlates with IVF successrates in women undergoing treatment with Adalimumab (Humira) and IVIG.Am J Reprod Immunol. 2011 June; 65(6):610-8.)

Moreover, the technique for determining the Th1/Th2 ratio requiresisolation of viable mononuclear cells from peripheral blood (PBMCs),stimulating them in vitro after having blocked cytokine secretion, astep that is somewhat toxic to the cells. Successful induction ofintracellular cytokine expression may thus be compromised and testresults theoretically less accurate. Further, while Th1 cellquantification appears relatively robust, Th2 quantification is hamperedby its low prevalence amongst CD4 positive T cells. Gating of the cellsas currently practiced can be somewhat subjective leading to impreciseresults. Assay results, therefore may vary because of minor gatingvariations. For these and other reasons, an improved technique forassessing Th1 and Th2 numbers is needed.

In addition, the predictive power of the different assays currentlybeing employed to assess pregnancy risk factors is not sufficientlysensitive to detect all affected individuals. Additional immune celltesting parameters are sorely needed. For example, currently two testsof natural killer cells are currently performed. The first is aphenotypic assay quantifying NK cells. Lymphocytes expressing CD56 butnot expressing CD3 are defined as NK cells and can be enumerated by flowcytometry. A second test assesses NK cell function whereby mononuclearcells are incubated with labeled cells known to be damaged by NK cellsor “target cells” are coincubated and subsequently detected andquantified by flow cytometry. Both of these tests can be used to assessa patient's risk of reproductive failure with some degree of success.However, some patients with normal natural killer cell results stillcontinue suffer from immunological—based pregnancy failure.

As noted, T regulatory cells (Treg) are a new and important cell typethat may help in diagnosis and assessment in many of these cases.Diminished numbers of these cells in peripheral blood have beenassociated with pregnancy loss particularly in the immediatepost-conceptual period. Jasper et al quantified FoxP3 mRNA, a masterregulator of T regulatory cell differentiation, in mid-secretoryendometrial tissues from women with unexplained infertility and levelsfrom unaffected women (Molecular Human Reproduction Vol. 12, No. 5 pp.301-308, 2006). They found reduced levels of FoxP3 mRNA in affectedwomen when compared with controls. Winger and Reed have found diminishedlevels of T regulatory cells in the peripheral blood lymphocytes definedby their concurrent expression of CD4, CD25 and FoxP3 in pregnant womenwho go on to early pregnancy loss. Winger E E, Reed J L. Low CirculatingCD4(+)/CD25(+)/Foxp3(+) T Regulatory Cell Levels Predict MiscarriageRisk in Newly Pregnant Women with a History of Failure. Am J ReprodImmunol. 2011 October; 66(4):320-8.)

A variety of markers are currently being employed for T regulatory cellsquantification, however, no comparable assay is available that permitsfacile functional assessment of T regulatory activity. Such an assay issorely needed.

In addition to lack of sensitivity demonstrated by the currentreproductive immunology assays, they can be vulnerable to specimencollection transportation conditions. The NK cytotoxicity assay isparticularly vulnerable. In the assay, effector cell activity is tested.Any stress upon the effector cells can be expected to diminish themeasured cytotoxic activity. Not withstanding these effects, targetcells are subject to significant variation in their vulnerability tocytotoxic effect. Thus, the assay system is subject to considerablevariability. An assay system that is not subject the variability of afunctional assay is also sorely needed.

The current functional assays are further limited in their inability todetect functional cell intermediaries. T regulatory cells exert theirregulatory activity through a number of intermediaries. The currentassays that enumerate the numbers of T regulatory cells do not identifynor quantify any intermediaries. An assay system that permitsrecognition and quantification of known intermediaries would provide asignificant improvement.

More recently, CD4 expressing T cells have been divided into additionalsubgroups based on their cytokine secreting profile. In addition to Th1and Th2 cell types, T regulatory, Th3 and Th17 cells have been defined.Also, Th9 and T follicular helper cells (T_(FH)) have been described.Jasper et al. have assessed the relative amount of FoxP3 mRNA in lutealphase endometrium and have shown distinct patterns distinguishingpatients with a history of recurrent loss from normal patients.Similarly quantification of mRNA for transcriptional regulators andcomparing them with control patients can provide information supportingthe classification of patients into candidates for immune-basedtherapies and subsequent monitoring of interventional therapy.

In addition to the aforementioned deficiencies of immune tests offunction and phenotype, specificity and sensitivity are known to belimited. An assay system that permits assessment of multiple differentparameters that together provide a profile or signature of the PBMCstatus of a patient might overcome deficiencies seen in single tests.The present testing methods assess individual characteristics of thepatient's immune system. A combinatorial approach wherein a number ofdifferent parameters are assessed together might improve bothsensitivity and specificity as well improve discrimination of differentforms of immune dysfunction that individually affect a single immuneparameter in the same manner. An approach broader than assessing immunestatus of PBMCs might provide better information as to the diagnosis andstatus of pregnancy disorders. Ideally, such an approach woulddiscriminate between different mechanisms resulting in a sharedabnormality in an immune or other parameter.

Personalized medicine, as understood, utilizes testing, in particulartesting at the level of DNA and RNA, to determine the most appropriatetherapeutic intervention for an individual rather than applying a singletherapeutic intervention to all patients with a particular complaint.Ideally, a diagnostic strategy would divide patients into categorieswhere patients are identified who would likely respond to a therapeuticintervention. Ideally, patients the testing strategy would identifypatients in whom a therapeutic strategy is unlikely to be of benefitthereby saving application of a costly therapy or a therapy withpotential risks to those patients who are unlikely to enjoy a positivetherapeutic response. Because of aforementioned deficiencies, it wouldbe useful to useful to identify more robust and stable surrogate markersfor the immunologic tests currently being performed in clinical practiceas well as identifying surrogate markers for inflammation andcoagulation markers.

In addition, new tests are needed that can identify patients who willnot benefit from therapy despite a positive disease diagnosis usingtraditional testing.

Also, new tests are needed that can predict which patients willexperience negative side effects from immunotherapy.

MicroRNAs are small, endogenous, non-protein encoding RNA sequences ofapproximately 22 nucleotide bases that predominantly negatively regulategene expression. Several hundred such sequences have been identified inhumans (Lee et al., PLoS Comput Biol 3:e67 (2007); O'Driscoll,Anticancer Res 26:4271 (2006); Kusenda et al., Biomed Pap Med Fac UnivPalacky Olomouc Czech Repub 150:205 (2006)). The primary transcript or“pri-microRNA” comprises one or more microRNA precursors each comprisedwithin a hairpin structure. These sequences are most commonly foundwithin the introns of their host genes (Lee et al., PLoS Comput Biol3:e67 (2007)). They may also be found within exons and acrossexon-intron boundaries (Kusenda et al., Biomed Pap Med Fac Univ PalackyOlomouc Czech Repub 150:205 (2006)). These sequences are known to targetat least 30 percent of all human genes, fine-tuning their expression.The final short sequence is generated through a series of cleavagesinvolving two enzymes, Drosha and Dicer, from relatively long RNAprimary RNA sequences. The final, cleaved form is incorporated into acomplex known as RISC (RNA induced silencing complex) that comprisescatalytic proteins such as Argonaut, specifically Ago-2. Jeker andBluestone (Journal of Clinical Immunology (2010), 30:347-357)hypothesize microRNAs act to stabilize cell phenotype, sharpen geneexpression, aid in setting thresholds amongst other regulatoryfunctions. microRNAs appear to be important regulators of cell growth,differentiation, and apoptosis (Lee et al., PLoS Comput Biol 3:e67(2007)). microRNAs have been extensively studied in cancer pathogenesisbecause of their known impact on cell dedifferentiation, growth, andapoptosis, each of which individually are important cellular events inthe development of cancer (Esau and Monia, Adv Drug Deliv. Rev. 59:101-114 (2007); Hammond, Nat Genet 39:582 (2007)). microRNA profileswithin cancer cells has been an area of intense study. Informationgained provides information about the functional state of individualcells.

Investigators have found that global expression of microRNA appears tobe more useful than mRNA expression in the classification of cancers(Eis et al. Proc Natl Acad Sci USA 102: 3627 (2005)). More recently, avariety of studies have demonstrated the importance of microRNA inhomeostasis and function of the immune system of B lymphocytes, Tlymphocytes, macrophages, dendritic cells and the heart (That et al.,Science 316:604 (2007); Rodriguez et al., Science 316:608 (2007);O'Connell et al., Proc Natl Acad Sci USA 104:1604 (2007); Care et al.,Nat Med 13:613 (2007); Taganov et al., Proc Natl Acad Sci USA I (2006).

MicroRNA 155 (mir-155) is exemplary of microRNA effects upon immunesystem function as well as immune cell differentiation. The microRNAsdisclosed herein are all of human origin, mir-155 acts to stabilize theexpression FoxP3 expression in T regulatory cells. (Foxp3-DependentMicroRNA155 Confers Competitive Fitness to Regulatory T Cells byTargeting SOCS1 Protein Immunity, Volume 30, Issue 1, Pages 80-91 L. Lu,T. That, D. Calado, A. Chaudhry, M. Kubo, K. Tanaka, G. Loeb, H. Lee, A.Yoshimura, K. Rajewsky) T regulatory cells are profoundly important inthe prevention of autoimmunity and for the establishment of foreigntissue tolerance. Further, That et al. have shown mir-155 plays a rolein regulating both T helper cell differentiation and the germinal centerreaction regulating the T cell-dependent antibody response (That et al.,Science 316:604 (2007); Rodriguez et al., Science 316:608 (2007).Further, transcriptosome analysis of microRNA-155-deficient CD4+ T cellsdemonstrate a wide spectrum of mir-155 regulated genes, includingcytokines, chemokines, and transcription factors (That et al., Science316:604 (2007); Rodriguez et al., Science 316:608 (2007).

mir-155 is exemplary of the pleitropism of microRNAs. Evidence to datehas shown that mir-155 is involved in numerous biologic processes. Theseinclude hematopoiesis, inflammation and immunity. It is also involved inregulation of the angiotensin II receptor. Deregulation of mir-155 hasbeen associated with certain cancers, cardiovascular disease as well asviral infections. T regulatory cell development as well as mediation ofFoxP3 effects may both involve mir-155 (Susan Kohlhaas, Oliver A.Garden, Cheryl Scudamore, Martin Turner, Klaus Okkenhaug, ElenaVigorito, Cutting Edge: The Foxp3 Target mir-155 Contributes to theDevelopment of Regulatory T Cells, The Journal of Immunology (2009)182:2578-2582). Surprisingly, these investigators found that while Tregdevelopment could not dispense with bic/mir-155, the pri-microRNA, itwas dispensable for Treg proliferation and survival in the periphery.Despite the lower number of Tregs, their suppressor function in vitroremained intact. Such unexpected actions are characteristic ofmicroRNAs. microRNAs can act at different time points in the developmentof cells and their subsequent functional activation. While they may actindividually, they may also act in concerted action of a plurality ofmicroRNAs on a single target mRNA. To add to the complexity of microRNAaction, individual microRNAs may also act on many different targetmRNAs. Together, these complexities make prediction of their actionunder differing circumstances tenuous. To date, only empiric methodshave defined the role of individual and multiple microRNAs underspecific disease conditions.

Other microRNAs are significantly associated with immune responses. Forexample, TNF alpha and IL-1 beta are regulated by yet another microRNA,mir-146a. (K. D. Taganov, M. P. Boldin, K. J. Chang and D. Baltimore,NF-B-dependent induction of the microRNA mir-146, an inhibitor targetedto signaling proteins of innate immune responses, Proc Natl Acad Sci USA103 (2006), pp. 12481-12486, M. M. Perry, S. A. Moschos, A. E. Williams,N. J. Shepherd, H. M. Larner-Svensson and M. A. Lindsay, Rapid changesin microRNA-146a expression negatively regulate the IL-1beta-inducedinflammatory response in human lung alveolar epithelial cells, J Immunol180 (2008), pp. 5689-5698.) Quantification of these microRNAs providesinsight into regulatory status. Further, studies in patients sufferingfrom rheumatoid arthritis display interesting microRNA profiles in theirPBMCs. (Kaleb M Pauley, Minoru Satoh, Annie L Chan, Michael R Bubb,Westley H Reeves and Edward K L Chan, Upregulated mir-146a expression inPBMCs from rheumatoid arthritis patients, Arthritis Research & Therapy2008, 10:R101 (doi:10.1186/ar2493) This article is online at:http://arthritis-research.com/content/10/4/R101) They observedsignificant increases in mir-146a, mir-155, mir-132, mir-16, mir-let-7arelative expression over normal controls as well as significantdifferences between active and inactive clinical states.

Hunter et al. (Hunter M P, Ismail N, Zhang X, Aguda B D, Lee E J, et al.(2008) Detection of microRNA Expression in Human Peripheral BloodMicrovesicles. PLoS ONE 3(11): e3694. doi:10.1371/journal.pone.0003694)find microRNAs circulating in peripheral blood in several compartments,plasma microvesicles, platelets and PBMCs. They suggest differing rolesfor microRNAs residing in the different compartments. microRNAs residentwithin PBMCs are most closely associated with CD4 expressing T cellsubclass specification and stable expression. Interrogation of microRNAsresident within the PBMC population is most likely to provideinformation regarding CD4 expressing T cells. Moreover, monocytes andcells of the monocyte lineage and of dendritic cells, one of the twoperipheral blood mononuclear populations, regulate the functional stateand activity of T cells and in particular CD4 expressing T cells.Importantly, monocytes participate in initiation of coagulationexpressing Tissue Factor on their surface. Mir-19b and mir-20a appear tomodulate tissue factor expression in patients with lupus (Raúl Teruel,Carlos Pérez-Sánchez, Javier Corral, María Teresa Herranz, VirginiaPérez-Andreu, Encarnación Saiz, Nuria García-Barberá, IreneMartínez-Martínez, Vanessa Roldán, Vicente Vicente, López-Pedrera,Constantino Martinez, Identification of microRNAs as potentialmodulators of tissue factor expression in patients with systemic lupuserythematosus and antiphospholipid syndrome, Journal of Thrombosis andHaemostasis, in press). Therefore, interrogation of microRNAs withinPBMCs as a whole provides significant information regarding the immunebalance and stability of circulating mononuclear cells.

Selective interrogation of subsets of mononuclear cells can impartadditional information. For example, removal of monocytes from isolatedPBMCs permits selective microRNA interrogation of lymphocytes.Conversely, monocytes can be interrogated directly for microRNAs.Further, lymphocyte subpopulations, themselves, can be individuallyinterrogated following their selective isolation by such techniques, forexample, flow cytometric sorting following interaction withfluorescently labeled monoclonal antibody combinations that are capableof discreetly characterizing the individual subclasses. For example, Tregulatory cells may be contacted under selective binding conditionswith fluorescently labeled anti-CD3, CD4, CD25 and CD127 and selected bytheir expression of CD3, CD4, CD25 and absence or low expression ofCD127.

MicroRNA provides information that is different than quantification oflymphocyte subsets, their function or markers of these subsets thatmight be surrogates for these subsets such as FoxP3 mRNA which might beregarded as a surrogate for quantification of T regulatory cells.microRNAs may be found in a variety of different cell types andrepresent different functions in different cell types or may representactivity states or other features that are not disclosed fromquantification of cell types or functional states (such ascytotoxicity). Alterations in microRNA expression varies from onedisease to another. The microRNA alterations noted in rheumatoidarthritis differ from those seen in lupus. (Pauley K M, Satoh M, Chan AL, Bubb M R, Reeves W H, Chan E K. Upregulated mir-146a expression inPBMCs from rheumatoid arthritis patients. Arthritis Res Ther. 2008;10(4):R101, Gastroenterol Hepatol (N Y). 2010 November; 6(11): 714-722.MicroRNA (microRNA) Expression in Ulcerative Colitis (UC) and Crohn'sDisease (CD) and Dai Y, Huang Y S, Tang M, Lv T Y, Hu C X, Tan Y H, Xu ZM, Yin Y B. Microarray analysis of microRNA expression in peripheralblood cells of systemic lupus erythematosus patients. Lupus. 2007;16(12):939-46., Raúl Teruel, Carlos Pérez-Sánchez, Javier Corral, MariaTeresa Herranz, Virginia Pérez-Andreu, Encarnación Saiz, NuriaGarcía-Barberá, Irene Martínez-Martínez, Vanessa Roldán, VicenteVicente, §hary López-Pedrera, Constantino Martinez, Identification ofmicroRNAs as potential modulators of tissue factor expression inpatients with systemic lupus erythematosus and antiphospholipidsyndrome, Journal of Thrombosis and Haemostasis, in press).

Aspects of immune disorders have implicated alterations in microRNAexpression. microRNAs have significant effects in the regulation ofimmunological functions and the prevention of autoimmunity (Kaleb M.Pauley, Seunghee Cha, and Edward K. L. Chan, MicroRNA in autoimmunityand autoimmune diseases, J Autoimmun. (2009) 32(3-4): 189-194).Anti-phospholipid antibody syndrome is an example of an autoimmunecondition associated with diminished fertility, recurrent unexplainedabortion and pregnancy complications as well as increased risk ofautoimmune, cardiovascular and thrombotic disease. Diminished expressionof certain microRNAs (mir-19b and 20a) may identify patients atincreased risk of pregnancy complications treatable by anticoagulationtherapy (e.g. aspirin and/or heparins) Teruel, Carlos Pérez-Sánchez,Javier Corral, María Teresa Herranz, Virginia Pérez-Andreu, EncarnaciónSaiz, Nuria García-Barberá, Irene Martínez-Martínez, Vanessa Roldán,Vicente Vicente, §hary López-Pedrera, Constantino Martinez,Identification of microRNAs as potential modulators of tissue factorexpression in patients with systemic lupus erythematosus andantiphospholipid syndrome, Journal of Thrombosis and Haemostasis, inpress)).

Identification of surrogate measures of the dysfunctional status ofpatients suffering reproductive disorders, it is recalled that pregnancyconstitutes an immunologic paradox where typical alloimmune responses totissues such as tissue/organ grafts are rejected, in the healthypregnancy tolerance to alloantigen prevails preventing the rejection ofthe hemialloantigenic embryo. Multiple and seemingly redundantmechanisms have been described that appear to maintain allo-tolerance.The existence of these mechanisms differentiate allo-responses to thefetus and autoimmunity. It is expected, therefore, that microRNApatterns identified amongst patients suffering reproductive disordersare unlikely to be similar to those identified in autoimmunity.Moreover, there appears to be no direct surrogacy of microRNAs forcurrently applied immune, coagulation tests currently being appliedbecause of their broad and varied presence and function in differentcell types.

Mishra (US patent application 20100216142 A1, Mishra; Nilamadhab,microRNA Biomarkers in Lupus) identifies a variety of microRNAs that arederegulated in lupus. Comprised amongst immune abnormalities seen inlupus are antibodies directed against various phospholipids andincreased levels of inflammatory markers such as tissue factor. A recentstudy by Ceribelli identifies antibodies directed against Ago2, acomponent of the RISC moiety required for the genesis of microRNAs(Angela Ceribelli, Angela Tincani, Franco Franceschini, RobertoCattaneo, Brad A. Pauley, Jason Y. F. Chan, Edward K. L. Chan,Anti-argonaute2 (Ago2/Su) and -Ro antibodies identified byimmunoprecipitation in primary anti-phospholipid syndrome (PAPS), Postedonline on Aug. 9, 2010 (doi:10.3109/08916934.2010.499886)). Intravenousimmunoglobulin (IVIG) therapy is an example an immune therapy used toreduce miscarriage incidence in women with a history of immunologicrecurrent spontaneous abortion. Winger and Reed have demonstratedefficacy of IVIG therapy in a group of women with recurrent spontaneousabortion when compared with a group of women not receiving IVIG therapy.(Winger E E, Reed J L, Ashoush S, El-Toukhy T, Ahuja S, Taranissi M.Elevated Preconception CD56(+) 16(+) and/or Th1:Th2 Levels PredictBenefit from IVIG Therapy in Subfertile Women Undergoing IVF. Am JReprod Immunol. 2011 May 30.) In addition, when patients withanti-phospholipid antibodies were excluded from the patient pool in thisstudy, significant differences could no longer be found between thetreated and non-treated groups (unpublished data). Therefore the abilityto identify patients with markers associated with antiphospholipidantibodies may be particularly useful when deciding who may benefit fromimmunologic treatment.

Detection of microRNAs in PBMCs and their constituent selected subsets,provides the clinician with an additional means of characterizingpatients with or at risk of reproductive immunologically-relateddisorder into groups whose disorder is mediated by an imbalance inimmune cell activity, inflammation or coagulation. Detection ofmicroRNAs in women with pregnancy disorders is taught by Taylor andGercel-Taylor (Taylor and Gercel-Taylor in US patent application20100151480 A1). They teach methods for diagnosis of cancer and adversepregnancy outcomes in a subject by measuring the amount of one or moreRNAs present in exosomes isolated from a biologic sample from theaffected individual. The methods they teach can be distinguished fromthe present subject matter. They teach that the microRNAs theyinterrogate are derived directly from placental tissues and, therebyrepresents the pathophysiologic state of that organ. The presentlydisclosed material teaches methods for defining the systemic and/orlocal immune status of the affected individual thereby providing analternative and supplemental means for assessing the immune status of anindividual that might adversely affect their reproductive health. Theaddition of such parameters should increase the sensitivity of adiagnostic panel. For example, mir-155 has been shown to be important inthe stabilization of FoxP3 expression and, moreover, for the effectorfunction of T regulatory cells, in part, through the regulation ofCTLA-4 expression (Lu et al. Immunity 30, 80-91 (2009)). Numerous andredundant mechanisms are postulated to induce a tolerant state in themother operative at various time points that include the egg development(oogenesis) period, preconceptual period, insemination, implantationthrough the remaining course of pregnancy and post-parturition. Eggquality may also be affected by immunologic events. A complex interplayof cytokines, hormones, growth factors are involved in oogenesis whichengage immune cell interactions. microRNAs quantification in immunecells may profile both a normal course and be distinguishable from thecourse in patients with reproductive and other immunologically relateddisorders.

MicroRNA quantification provides additional benefits. Were patterns ofresponse to immunotherapies identified, they could be used to predictresponse patterns amongst patient subgroups identified by their microRNAprofiles. Were specific microRNAs or groups of microRNAs identifiedwhose responses following therapies could be used to dichotomize ormultiperize patient groups, then patients so grouped could be assessedaccording to the responses to therapies and used to more preciselypredict responses of individual patients so grouped.

The ability to separate patients into groups of two or more isimportant. The use of Herceptin to treat breast cancer could not beshown to be effective when used in otherwise clinical similar breastcancers. Herceptin (trastuzumab) targets the HER2/neu receptor. In earlybreast cancer, presence or absence of the receptor is not clinicallyapparent. However, when expressed by a breast cancer, it denotes a classof tumors with more aggressive future behavior. Molecular techniques canbe used to assess the HER2/neu status of the tumor. Clinical studiesrevealed that when use of the drug was restricted to those patientswhose tumor was positive for HER2/neu expression, the drug was shown tobe effective. The identification of microRNA profiles comprising one ormore individual microRNAs could be of similar importance in separatingpatients into separate groups otherwise indistinguishable that could beassessed to define groups of individuals with similar drug responses.

This invention accomplishes these and other goals.

SUMMARY OF THE INVENTION

In accordance with the above needs and those that will be mentioned andwill become apparent below, this disclosure is directed to a method foridentifying at least two characteristic groups in a patient populationon the basis of microRNA expression including the steps of collectingimmune cells, extracting microRNA-comprising RNA from the immune cells,quantifying at least one microRNA within the extracted RNA, andsegregating the patient population into the groups on the basis ofexpression of the at least one microRNA. Preferably, the step ofcollecting immune cells comprises collecting peripheral bloodmononuclear cells.

In one aspect, the step of segregating the patient population includesassigning patients expressing a relatively high level of the at leastone microRNA to a first group and assigning patients expressing arelatively low level of the at least one microRNA to a second group.Collecting immune cells may include collecting cells before or after animmunotherapy treatment. A further aspect is directed to collecting thecells before and after an immunotherapy treatment such that segregatingthe patient population includes determining the change in expressionlevel of the at least one microRNA after the immunotherapy treatment.Preferably, segregating the patient population comprises assigningpatients exhibiting a first change in the expression level to a firstgroup and assigning patients exhibiting a second change in theexpression level to a second group. The first change may be a relativelylarge change in expression level and the second change may be arelatively small change in expression level. Alternatively, the firstchange may be a positive change in expression level and the secondchange may be a negative change in expression level.

Preferably, the absolute value of the mean of the change in theexpression level of the at least one microRNA in the first group dividedby the standard deviation is greater than or equal to one. Further, oneembodiment is directed to the further step of identifying a subset ofmicroRNAs within the group of known microRNAs that exhibit a change inexpression level in the first group such that the absolute value of themean of the change in expression level divided by the standard deviationis greater than or equal to one. In addition, the method can alsoinclude identifying a microRNA within the group of known microRNAs thatexhibits the greatest change in expression level in the first group.

Another embodiment of the invention is directed to the additional stepsof collecting immune cells from an additional patient, extractingmicroRNA-comprising RNA from the immune cells of the additional patient,quantifying at least one microRNA within the extracted RNA from theadditional patient, and identifying the additional patient as belongingto one of the segregated groups on the basis of expression of the atleast one microRNA. Preferably, this also include administering atreatment to the additional patient based on the identification, such asIVIG.

In another aspect, the methods of the invention also include diagnosinga patient as having a condition based on membership in a segregatedgroup. One embodiment is directed to diagnosing a patient having areproductive disorder.

Yet another aspect of the invention includes the additional step ofmonitoring treatment of a patient belonging to one of the segregatedgroups by collecting immune cells, extracting at least one microRNA andquantifying the at least one microRNA at a subsequent time.

Presently preferred embodiments of the invention include the use of atleast one microRNA selected from hsa-let-7e, hsa-mir-1181, hsa-miR-1183,hsa-miR-1224-5p, hsa-miR-127-3p, hsa-mir-1296, hsa-mir-132, hsa-mir-136,hsa-miR-139-3p, hsa-mir-141, hsa-miR-142-3p, hsa-mir-142-5p,hsa-mir-144, hsa-mir-153, hsa-mir-1537, hsa-miR-154, hsa-miR-191,hsa-mir-193a-3p, hsa-miR-19a, hsa-mir-219-5p, hsa-mir-29b, hsa-mir-301a,hsa-miR-301b, hsa-miR-30e, hsa-mir-32, hsa-mir-33a, hsa-miR-340,hsa-miR-362-3p, hsa-miR-371-5p, has-377, hsa-miR-423-3p, hsa-miR-432,hsa-mir-513a-5p, hsa-mir-545, hsa-miR-548a-5p, hsa-miR-574-5p,hsa-mir-582-3p, hsa-mir-590-5p, hsa-mir-15a, hsa-mir-548c-5p,hsa-mir-1225-3p, hsa-mir-29b, hsa-mir-21, hsa-mir-1237, hsa-mir-101,hsa-mir-1539, hsa-mir-557, hsa-mir-125a-3p and hsa-mir-423-5p. Morepreferably, the microRNA is selected from hsa-mir-136, hsa-mir-141,hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537, hsa-mir-193a-3p,hsa-mir-219-5p, hsa-mir-29b, hsa-mir-301a, hsa-mir-32, hsa-mir-33a,hsa-mir-545, hsa-mir-582-3p, hsa-mir-590-5p, hsa-mir-1181,hsa-mir-513a-5p, hsa-mir-132 and hsa-mir-1296.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features and advantages will become apparent from the followingand more particular description of the preferred embodiments of theinvention, as illustrated in the accompanying drawings, and in whichlike referenced characters generally refer to the same parts or elementsthroughout the views, and in which:

FIG. 1 shows the CT levels of the patients having high initial microRNAexpression before and after IVIG treatment, and

FIG. 2 shows the CT levels of the patients having low initial microRNAexpression before and after IVIG treatment.

DETAILED DESCRIPTION OF THE INVENTION

At the outset, it is to be understood that this disclosure is notlimited to particularly exemplified materials, architectures, routines,methods or structures as such may, of course, vary. Thus, although anumber of such option, similar or equivalent to those described herein,can be used in the practice of embodiments of this disclosure, thepreferred materials and methods are described herein.

It is also to be understood that the terminology used herein is for thepurpose of describing particular embodiments of this disclosure only andis not intended to be limiting.

This summary provides a listing of several embodiments of the presentlydisclosed subject matter. However, it should be understood thatvariations and permutations of these embodiments exist. This summary isintended to serve as exemplary of potential embodiments.

The term “immune cells” as used herein shall mean lymphocytes, monocytesand granulocytes, their precursors and maturational derivatives. Theseshall include, for example, plasma cells, dendritic cells, mast cells,granulocytes and macrophages. It is understood that immune cellsparticipate in a broad range of activities. These include immunologicsurveillance and intervention (such as elimination of malignant cellsand elimination of infectious agents). Moreover, cells of the immunesystem, in particular monocytes and their derivatives are involved incoagulation. Further, it is understood inflammation is a manifestationof activities of cells of the immune system.

As used herein, the term “immunotherapy”, “immunotherapeutic” or “immunetherapy” shall include therapeutic intervention directed to modificationof activities of cells of the immune system thereby contemplatingactions affecting immune cells wherein said cells affect coagulation andinflammation.

Herein, specific microRNAs are identified by their prefix mir- and theiridentifier, such as mir-155 in this case. Specific microRNAabbreviations may also include an additional prefix identifying thespecies of origin, such as hsa for homo sapiens. Although the primaryembodiments described herein are directed to humans, one of skill in theart will appreciate that the techniques of this disclosure can beapplied to other species.

The term “control individual” as used herein has a special meaning. A“control individual” shall mean individuals of comparablecharacteristics such as age and sex who do not have a reproductivedisorder and are not at known risk of developing a reproductivedisorder. The term “control sample” as used herein shall mean a biologicsample from the same source, such a peripheral blood, and collectedunder the same or comparable conditions as a patient sample comprisingimmune cells collected from a control individual that is processed andanalyzed in the same manner as a patient sample.

It is further understood that the term “control sample” as used hereinmay represent the mathematical mean of multiple samples from controlindividuals wherein a number of samples considered sufficient by anindividual of ordinary skill in the art are collected. Additionalstatistical parameters may be derived from said samples such as standarddeviation of the mean. Said additional statistical parameters may beused for purposes of comparison of a patient test result with controlsamples to estimate the probability that the patient's test resultrepresents an abnormal finding and, thereby suggests that the patient issuffering from a reproductive disorder or risk of a reproductivedisorder. For purposes of simplicity the term may also be used inanother way wherein a plurality of comparable, temporally separate,samples are collected and assayed from a single individual and comparedwith one another such that a first sample or a particular subsequentsample are compared as though the first is a control for the second,permitting assessment of a change in condition potentially as a functionof the clinical state, or stage of pregnancy or as a result oftherapeutic intervention.

The term “reproductive immune dysfunction” or “reproductive disorder” asused herein shall comprise those disorders of the reproductive systemsuspected of having an immune component. These shall include but notlimited to the following: infertility and post-conceptive failure suchas implantation failure that may be unrecognized and thereby diagnosedas infertility; miscarriage; conditions that do not lead to miscarriagebut compromise optimal pregnancy outcome such as intrauterine growthretardation, PROM (Premature Rupture Of Membranes), pre-eclampsia,preterm labor, placental abruption and stillbirth; those conditionsknown to contribute to infertility, pregnancy complications and earlyimplantation failure such as endometriosis and autoimmune thyroiditisand anti-phospholipid antibody syndrome, those pregnancy disorders thatcompromise optimal fetal growth, maturation and development in pregnancyand/or compromise potential childhood development after delivery, thosereproductive disorders that compromise the long term reproductivepotential of the mother over the course of her reproductive lifespan.

The term “Immunologic disorder” as used herein shall comprise disorderscaused by abnormal, whether humoral, cell-mediated, or both and/orrelated to an immune component such as an inflammatory, complement orcoagulation-mediated component.

As used herein, the term “differentially expressed” or “differentialexpression” shall mean a detectable difference by the selected detectionmeans in the quantification of a specific microRNA between the biologicsample of the patient and the corresponding mean value of a controlpopulation wherein said difference has been identified between astatistically significant patient population with a reproductivedisorder and a corresponding control population without the disorder orrisk of the disorder. The terms may also be applied whereinquantification of a plurality of individual microRNAs form a patternthat can be distinguished from a corresponding pattern identified incontrols. Differential expression of one or more microRNAs betweenpatients with a reproductive disorder and/or risk of a reproductivedisorder and control individuals is preferably determined in a screen ofa panel of microRNAs such as that provided by SABiosciences (catalogMAH-104A). Differentially expressed microRNAs between patient andcontrol values may be determined by a variety of means. Each methodrequires inclusion of a minimum number of samples from each group sothat a significant difference in expression between the two groups canbe ascertained. A preferred embodiment to define differentiallyexpressed microRNAs between patients and controls utilizes the microRNAhuman immunopathology-related microRNA array (SuperArray technology,SABiosciences, Frederick, Md. catalogue MAH-104A for StratageneMx3005p). Reactions of three or more RNA extracts from patients with areproductive disorder or at risk of a reproductive disorder and three ormore control samples are performed following the manufacturer'sinstructions. The quantitative PCR is run on a Stratagene 3005preal-time thermocycler following the manufacturer's instructions. Foreach set of triplicates or greater, the mean value for each microRNA isdetermined and used to calculate the differences in levels. A microRNAvalue is determined to be differentially expressed when there is adifference between patient value and control value with a P value of≦0.05. Other P values may be selected as determined by someone withordinary skill in the art. It should be understood that a reproductiveimmunologic disorder or risk of such a disorder can comprise differentpatterns of differential expression. Further, the time at which apatient is tested may result in a different pattern of microRNAexpression and further, patterns may differ with respect to ongoingtherapy. For example, T regulatory cells physiologically increasefollowing conception. It would be expected that microRNAs such asmir-155 which is closely involved in regulation of FoxP3, atranscriptional regulator involved in T regulatory function, would bedifferentially regulated between pre and post conception.

The term “bimodal” or “bimodal distribution” will have the meaningcommonly understood by those of ordinary skill in the art of statistics.Histograms of data that comprise two peaks are referred to as “bimodal”while those with a single peak are referred to as “unimodal”. ErhardReschenhofer of the University of Vienna in the Journal of StatisticsEducation, 9(1) (2001)http://www.amstat.org/publications/jse/v9nl/reschenhofer.html)downloaded Oct. 24, 2011) formally defines “bimodality” and providesstatistical tests for determining bimodality. It is clear that not alldistributions with two peaks, particularly overlapping peaks arebimodal. They must be clearly separable. Bimodality can be assessed lessformally by inspection of a histogram of the data. Where there issignificant overlapping of the ranges as determined from the means andstandard deviations of the two peaks, bimodality cannot be claimed.

The embodiments discussed herein are primarily discussed in terms ofbimodality such that dichotomous groups exist. However, it should beunderstood that if multiple patient groups are distinguishable using thetechniques of the disclosure, the principles will still apply.

As used herein the term “making a diagnosis” or equivalent term as usedherein shall refer to the aggregate of methods used by an individual,preferably a physician skilled in the art of reproductive medicine,shall mean predicting a clinical outcome with or without treatment,selecting treatment and monitoring treatment utilizing measurement ofone or more microRNA levels or profiles derived from immune cellscomprised in one or more biologic samples of the patient and comparedwith appropriate controls. It is further understood that said diagnosismay involve concomitant assessment of other clinical findings togetherwith said assessment of microRNA quantification.

Unless otherwise indicated, all numbers expressing quantities ofingredients, reaction conditions, and so forth used in the specificationand claims are to be understood as being modified in all instances bythe term “about”. Accordingly, unless indicated to the contrary, thenumerical parameters set forth in this specification are approximationsthat can vary depending upon the desired properties sought to beobtained by the presently disclosed subject matter.

As used herein, the term “about,” when referring to a value or to anamount of mass, weight, time, volume, concentration or percentage ismeant to encompass variations of in some embodiments ±20%, in someembodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, insome embodiments ±0.5%, and in some embodiments ±0.1% from the specifiedamount, as such variations are appropriate to perform the disclosedmethods.

All patents and references cited herein are incorporated in entirety byreference. All technical and scientific terms used within thisdescription shall have the same meaning as commonly understood by thoseof ordinary skill in the art disclosed herein except where otherwisespecifically defined. Following long-standing patent law convention, theterms “a”, “an”, and “the” refer to “one or more” when used in thisapplication, including the claims. Thus, for example, reference to “apeptide” includes a plurality of such peptides, and so forth.

The presently disclosed subject matter comprises a method for diagnosisof patients with reproductive immunological disorders. It is understoodthat these disorders additionally may have inflammatory and coagulationaspects. The method comprises providing a biological specimen whereinsaid specimen comprises cells of the immune system, separating saidcells and isolating microRNA from said cells and quantifying the amountsof one or more microRNAs comprised within said cells and comparing theamount of one or more microRNAs to one or more microRNA control levels.The subject is diagnosed as having a reproductive immunologic disorderif the amount of one or more microRNA is differentially expressed.

Another aspect of the invention is the use of microRNAs to distinguishpatients whom are likely to respond to a therapeutic intervention fromthose who are unlikely to respond. In particular, specific microRNAs mayidentify patients whom are likely to respond to intravenousimmunoglobulin therapy (WIG) from those who are not likely to respond.It is understood that such patient identification extends to patientsnot suffering reproductive disorders but rather to include all patientsin whom IVIG therapy is contemplated. MicroRNA monitoring can beextended to the monitoring of IVIG efficacy in those patient candidatesfor IVIG therapy for other conditions. Moreover, the invention may beutilized in the same manner to determine the suitability of otherimmunotherapeutic agents for example a TNFα blocker such as Humira aswell as steroids, intralipid, lymphocyte immunization and IL-1 blockers(Anakinra).

Said method constitutes assessment of the quantity of one or moremicroRNAs wherein quantification of said microRNAs clinically correlatesto microRNA present in cells of the immune system within the biologicsample. Thus, wherein a biologic sample is whole blood and microRNA isextracted from whole blood, so long as quantification of individualmicroRNAs correlates clinically with microRNA present within cells ofthe immune system, is regarded as quantification of microRNA whereinsaid microRNA is isolated from cells of the immune system isolated fromwhole blood. Quantification of microRNAs from cells of the immune systemmay require normalization to a standard such as a housekeeping gene.Quantification as meant herein contemplates the use of an internalstandard.

In an embodiment of the presently-disclosed subject matter, a method fordiagnosis of women with reproductive disorders is disclosed wherein asingle microRNA known to be differentially expressed in patientsaffected by a reproductive disorder is interrogated. The method involvesisolating microRNA from immune cells isolated from a biologic specimenand identifying a microRNA known to be differentially expressed inpatients with a reproductive disorder comprised within said cells andcomparing the subject's microRNA to a microRNA control. The subject isdiagnosed as having a reproductive disorder if the microRNA isdifferentially expressed.

In an embodiment of the presently-disclosed subject matter, a method fordiagnosis of women with reproductive disorders is disclosed wherein aplurality of microRNAs known to be differentially expressed in patientsaffected by a reproductive disorder are interrogated. The methodcomprises providing a particular microRNA profile identified with areproductive disorder or risk of reproductive disorders. The methodinvolves isolating microRNA from said cells and identifying a microRNAprofile comprised within said cells and comparing the subject's microRNAprofile to a microRNA control profile. The subject is diagnosed ashaving a reproductive disorder if said microRNA profile is seen to bepresent.

In an embodiment of the presently-disclosed subject matter, a method fordiagnosis of women with reproductive disorders is disclosed wherein oneor more microRNAs markers are selected from the group consisting ofmir-155, mir-146a, mir-16-1, mir16-2, let7a-1, let7a-2, let7a-3, let7e,let7g, mir-132, mir-9, mir-142-3b, mir-17-92, mir-223, mir-181a areinterrogated. The method comprises providing a particular microRNAprofile identified with a reproductive disorder or risk of reproductivedisorders. The method involves isolating microRNA from said cells andidentifying a microRNA profile comprised within said cells and comparingthe subject's microRNA profile to a microRNA control profile. Thesubject is diagnosed as having a reproductive disorder if said microRNAprofile is seen to be present.

In another embodiment of the presently-disclosed subject matter, amethod for evaluating treatment efficacy and/or the progression of areproductive disorder is disclosed. The process comprises provision of aplurality of biological specimens over a period of time wherein saidspecimen comprises cells of the immune system, separating said cells andisolating microRNA from said cells and quantifying the type and amountof multiple microRNAs comprised within said cells and comparing thismicroRNA profile to one or more microRNA control profiles for thepurpose of determining differential expression of the profiles, therebypermitting assessment of the progress of the condition or the efficacyof therapy.

Subjects may be human or other animal. “Reproductive disorders” compriseone or more disorder selected from a group exemplified by but is notlimited to premature rupture of membranes, preeclampsia, preterm birth,intrauterine growth restriction, and recurrent pregnancy loss andanti-phospholipid antibody syndrome.

Immunologic dysfunction includes immunologic disorders caused byabnormal immunologic mechanisms, whether humoral, cell-mediated, or bothand/or is related to an immune related mechanism such as aninflammatory, complement or coagulation-related condition.

Immunologic dysfunction may increase the risk of bearing children wholater develop conditions, for example, asthma, autism, attention deficithyperactivity disorder (ADHD), Tourette's syndrome, diabetes andschizophrenia wherein said immunologic dysfunction are included withinthe concept of reproductive and/or immunologic disorders. The inventionalso contemplates peri-pregnancy periods comprising about one yearpreceding or following pregnancy. However, it is within the scope ofthis invention to include the period of time involved in oogenesis. Thisperiod may exceed one year prior to pregnancy. As noted in Winger andReed, the period prior to pregnancy may constitute an immunologic stateadverse to pregnancy outcome (Winger E E, Reed J L, Ashoush S, Sapna A,El-Toukhy T, Taranissi M: “Treatment with adalimumab (Humira) andintravenous immunoglobulin (IVIG) improves pregnancy rates in womenundergoing IVF. American Journal of Reproductive Immunology, 2009;61:113-120). Likewise the period following pregnancy may be affected asin, for example, autoimmune diseases such as rheumatoid arthritis, adisorder known to flare following pregnancy. A biologic specimencomprising cells of the immune system from a subject suspected of areproductive disorder is processed wherein said cells are isolated by avariety of means known to those skilled in the art. In a preferredembodiment, the biologic sample is whole blood. In a more preferredembodiment, cells from the blood are isolated by Ficoll-hypaque densitygradient centrifugation in the method taught by Boyum (Boyum A 1983.Isolation of human blood monocytes with Nycodenz, a new non-ioniciodinated gradient medium. Scand J Immunol 17: 429-436). RNA isextracted utilizing a method suitable for extracting short RNAsequences. Preferably said method utilizes a kit optimized for recoveryof microRNA sequences such as mirNeasy Mini Kit Qiagen catalogue 217004following instructions provided. Quantification of microRNA may bedetermined a variety of techniques known to those skilled in the art. Ina preferred embodiment, individual microRNAs are quantified by real-timepolymerase chain reaction (PCR). In a more preferred embodiment, a kitprovided by SABiosciences provide reagents and methods for individualmicroRNA's known to be involved in human immunopathologic conditions forsaid quantification optimized for specific real-time thermocyclingequipment such as the Stratagene Mx3005p (catalog MAH-104A)(www.SABioscies.com). Operating instructions for the Stratagene Mx3005pare provided by the manufacturer. Comprised therein are instructions forspectrophotometric quantification of recovered RNA, recommendations forinput quantity of RNA and PCR master mix. Quantification may beperformed concurrently with quantification of a “housekeeper gene” (agene that is expressed with relative constancy in the cells beinginterrogated thereby permitting relative quantification). Housekeepinggenes may be selected from, for example, beta actin,glyceraldehyde-3P-dehydrogenase (GAPDH), annexin A2 (ANXA2), glutathioneS-transferase (GST), ornithine decarboxylase (ODC), hypoxanthinephosphoribosyltransferase (HPRT1), ubiquitin (UBQglyceraldehyde-3P-dehydrogenase (GAPDH), annexin A2 (ANXA2), glutathioneS-transferase (GST), ornithine decarboxylase (ODC), hypoxanthinephosphoribosyltransferase (HPRT1), ubiquitin (UBQ), 18s RNA. Theresulting ratio comprises a relative quantity that is independent of thequantity of RNA input into the assay system. This permits comparisonwith a control sample quantified in a similar manner as a ratio of theanalyte signal and the selected housekeeping gene.

microRNAs of interest known to participate in immunopathologicconditions comprise but are not limited to mir146a, 1 mir-46b, mir-155,mir-605, mir-623, mir-583, mir-26a, mir-519d, 1 mir-26, 1 mir-6, 3mir-69-3, Let-7a and 125b mir-126, mir-155, mir-21, Let-7a, let-7c,let-7d, let-7e, let-7g, mir-214, mir-409-3p, mir-451, mir-103, mir-105,mir-106a, mir-125a-5p, mir-125b, mir-126, mir-128, mir-130a, mir-132,mir-134, mir-135a, mir-135b, mir-138, mir-142-3p, mir-142-5p, mir-143,mir-145, mir-147, mir-148a, mir-149, mir-150, mir-15a, mir-15b, mir-16,mir-181a, mir-183, mir-184, mir-185, mir-187, mir-18a, mir-191, mir-195,mir-196a, mir-198, mir-19a, mir-19b, mir-200a, mir-203, mir-205,mir-206, mir-20a, mir-20b, mir-21, mir-214, mir-223, mir-23b, mir-26a,mir-26a, mir-26b, mir-27a, mir-27b, mir-298, mir-299-3p, mir-29b,mir-29c, mir-302a, mir-302c, mir-30b, mir-30c, mir-30e, mir-31, mir-325,mir-335, mir-34a, mir-369-3, mir-370, mir-379, mir-383, mir-409-3p,mir-46b, mir-493, mir-519d, mir-574-3p, mir-577, mir-583, mir-605,mir-623, mir-9, mir-98, mir-99b.

In a preferred embodiment, one or more microRNAs known to bedifferentially expressed from control sample values are used. Alsopreferably, a plurality of microRNAs are identified as indicating apattern or signature of differentially expressed microRNAs.Determination of said single or plurality of microRNAs may be determinedby quantifying microRNA levels in both patients suffering fromreproductive disorders or at risk of reproductive disorders in panels ofmicroRNAs and comparing microRNA determining from biologic samples ofpatients levels with those derived from control samples.

The invention may be used to suggest the suitability of subjects withreproductive and/or immunologic disorders for specific immunotherapiesthat might mitigate such disorders and may also be used to assess apatient's risk for developing such disorders. Further, the invention maybe used to monitor the progress of the disease treatment or monitorreduction in disease risk by providing a series of assays and comparingthe results. Serial studies may be performed during, prior to andfollowing pregnancy as well as during the course therapy and compared todetermine the success and adequacy of the therapy provided.

Quantification of microRNA from PBMCs permits the investigator orclinician to compare the result to the appropriate control wherein adifference in expression is identified. For example, wherein an microRNAsuch as mir-155 in a patient with a reproductive disorder or risk ofsuch disorder such as recurrent abortion, is differentially expressedfrom a control comprised of individuals who have not experiencedrecurrent abortion, the patient may be diagnosed as having areproductive disorder and that the patient is a candidate forimmunologic intervention such as with a TNF alpha blocker. Patients sodiagnosed may be treated as described for patients defined as havingsimilar disorders by Winger and Reed (Edward E. Winger, Jane L. Reed,Treatment with Tumor Necrosis Factor Inhibitors and IntravenousImmunoglobulin Improves Live Birth Rates in Women with RecurrentSpontaneous Abortion, 60(1), 8-16, Published Online: 28 Jun. 2008,Edward E. Winger, Jane L. Reed, Sherif Ashoush, Sapna Ahuja, TarekEl-Toukhy, Mohamed Taranissi, Treatment with Adalimumab (Humira) andIntravenous Immunoglobulin Improves Pregnancy Rates in Women UndergoingIVF, American Journal of Reproductive Immunology 61 (2009) 113-120)

In some embodiments, the cells from which the RNA is extracted shall bePBMCs while in others cells shall be immune cells derived from bodytissues such as endometrium, decidua, fetal and placental tissues andsecondary lymphoid organs such as lymph node. Mononuclear cells may befurther selected by a variety of techniques, for example, by flowcytometric sorting following labeling of the cells with markers such asmonoclonal antibodies that permit their segregation into immune cellsubtypes. For example, T regulatory cells can be selected followingtheir labeling by monoclonal antibodies such as CD4, CD127 and CD25(with or without the addition of CD3) according to the Becton DickinsonCo. (utilizing reagents and equipment from the company according tohttp://www.bdbiosciences.com/research/tcell/regulatorytcells/workflow/identifyingtregs.jspdownloaded Apr. 28, 2010. RNA may be extracted from cells isolated cellsselected by said means may be prepared by extraction according toinstructions from the manufacturer (Qiagen catalogue 763134). microRNAsuch as for mir-155 may be detected and quantified by PCR (polymerasechain reaction) by the method described by Chen et al.(http://www3.appliedbiosystems.com/cms/groups/mcbmarketing/documents/generaldocuments/cms_(—)040548.pdfdownloaded May 11, 2010).

Primers and reagents may be selected for individual microRNAs from thosedescribed in product overview(http://www3.appliedbiosystems.com/cms/groups/mcb_marketing/documents/generaldocuments/cms_(—)068884.pdfdownloaded May 11, 2010). This document provides information teachingthe detection and quantification of individual microRNAs.

The method comprises providing a biological sample from a subject with ahistory of reproductive and/or immunologic disorder or risk of suchdisorder said sample being derived from immune cells, for example,derived from peripheral blood, and then isolating mononuclear cells astaught by Boyum (Boyum A 1983. Isolation of human blood monocytes withNycodenz, a new non-ionic iodinated gradient medium. Scand J Immunol 17:429-436) and then determining the amount of non-coding RNA such aspreferably microRNA (microRNAs) and comparing to the amount of thecorresponding RNA in the sample to similarly treated biological samplefrom control individuals. In addition, the method can comprisequantification of a plurality of individual microRNAs from thebiological sample and quantifying the individual microRNAs and comparingthe amount of microRNAs to corresponding microRNA control levels. Thesubject is then diagnosed as having a reproductive and/or immunologicdisorder or risk of developing such a disorder if there is differentialexpression in the amount of one or more of the RNAs from the sample ascompared to corresponding RNA control levels. In some embodiments, themethod further comprises selecting a treatment or modifying a treatmentbased on the amount of the one or more RNAs determined. Thisdetermination may be based upon assessment of specific individual orcombinations of the individual microRNAs.

In other embodiments of the presently-disclosed subject matter, a methodfor evaluating treatment efficacy and/or progression of a patient with areproductive or immunologic disorder and/or risk of developing areproductive disorder in a subject is provided. In some embodiments, themethod comprises providing a series of biological samples over a timeperiod from a subject, isolating the RNA as described above comprisingRNAs from the series of biological samples, determining an amount of oneor more of the microRNAs in each of the biological samples from theseries and determining any measurable change in the amounts of the oneor more microRNAs in each of the biological samples from the seriespermitting a measure of treatment efficacy and/or progression of thereproductive disorder or risk of reproductive disorder in the subject.

In still other embodiments of the presently-disclosed subject matter, amethod for characterizing a reproductive disorder in a subject isprovided. In some embodiments, the method comprises providing abiological sample from a subject, isolating the RNA comprising microRNAsfrom the biological sample, determining an amount of one or more of theRNAs and comparing the amount of one or more microRNAs to correspondingmicroRNA control levels. The reproductive disorder is then characterizedbased on differential expression of the amount of the one or moremicroRNAs from the sample as compared to the one or more microRNAcontrol levels. In some embodiments, the reproductive disorder ischaracterized when compared with well characterized individuals withknown reproductive disorders.

In some of these methods, quantification of microRNAs comprises using areal-time polymerase chain reaction. SABiosciences provide reagents andmethods for individual microRNA's known to be involved in humanimmunopathologic conditions for said quantification optimized forspecific real-time thermocycling equipment such as the StratageneMx3005p (catalog MAI-I-104A) (www.SABioscies.com). Quantification may beperformed concurrently with quantification of a “housekeeper gene” (agene that is expressed with relative constancy in the cellular materialbeing interrogated permitting standardization). Further, in someembodiments of these methods, the mirRNAs are one or more microRNAs.Amongst microRNAs quantified include but are not limited.

Various polymorphisms are known to exist within a specific microRNA, andalso within short sequence comprised within the microRNA known as the“guide sequence” as well as polymorphisms within the hairpin structurein which the microRNA is comprised. Further polymorphisms may be foundin the target RNA sequence. These polymorphisms may be result in alteredefficiency of microRNA regulation of target mRNAs. (Jazdzewski K, MurrayE L, Franssila K, Jarzab B, Schoenberg D R, de la Chapelle A. Common SNPin pre-mir-146a decreases mature mir-expression and predisposes topapillary thyroid carcinoma. Proc Natl Acad Sci USA 2008; 105:7269-74.)Further, Polymorphisms may potentially affect microRNA-mediatedregulation of the cell can be present in the 3′-UTR of a microRNA targetgene. Additional polymorphisms may also be present in the genes involvedin microRNA biogenesis as well as in pri-, pre- and mature-microRNAsequences. The consequences of such polymorphisms in processed microRNAsmay have profound effects on the expression of a multiplicity of targetgenes and have serious consequences, whereas a polymorphism in microRNAtarget site, in the 3′-UTR of the target mRNA, may be more target and/orpathway specific (Prasun J Mishra and Joseph R Bertino, MicroRNApolymorphisms: the future of pharmacogenomics, molecular epidemiologyand individualized medicine, Pharmacogenomics. 2009 March; 10(3):399-416.doi: 10.2217/14622416.10.3.399).

Detection of such polymorphisms by such techniques as real-time allelediscrimination, for example, is also within the scope of this invention.Methods can be found in Mx3000P instruction manual(www.bio.davidson.edu/courses/Bio343/Mx3000P_Manual.pdf downloaded Oct.24, 2010)).

The invention is particularly well suited for use in personalizedmedicine. Nucleic acid characterization and quantification are used toassess the probability of success of a particular therapeuticintervention. It is the goal of personalized medicine to identifypatients whom are likely, or conversely unlikely to respond to acandidate therapy. Cost, side-effects and improved therapeutic responseare accepted reasons for pursuing nucleic acid testing as a means ofselecting therapies and for following the course of therapy. Not onlymight quantification of microRNAs be helpful in identifying patientssuffering from reproductive disorders, but such quantifications would beof corresponding assistance in selecting and directing therapeuticchoices and monitoring their effects in a virtually unlimited variety ofdisorders.

The ability to separate patients into groups of two or more bycharacteristics of their response to an intervention such as for exampleimmunotherapy for example IVIG, permits more specific prediction oftherapeutic response to the specific intervention and also may permitprediction of response to other interventions. Another aspect of suchseparation is better prognostication and vulnerability to otherdisorders for example autoimmune diseases. If a microRNA were identifiedwhose response pattern amongst the patient cohort was bimodal, thenpatients could be grouped according to their response into said groups.

The invention comprises collecting immune cells, preferably PBMCs,before and after an intervention, preferably immunotherapeutic, forexample IVIG, extracting microRNA-comprising RNA from said cells,quantifying microRNAs within the extracted RNA, determining whether oneor more microRNAs quantified display a bimodal response amongst astatistically sufficient number of patient samples. If a bimodalresponse pattern is demonstrated in one or more microRNAs, then patientsmay be segregated into groups according to their response.

A “response” as used herein is defined as the difference between aresult on a first sample and a second sample wherein there is anintervening intervention or the intervening effect of an interventionpreviously made. It may be an increase, a decrease or an absence ofchange. The term “bimodal” or bimodality” as used herein refers to anon-normal distribution of responses wherein two distinct modescharacterize the data. The data can be displayed graphically in ahistogram and bimodality assessed by inspection. Statistical methodshave been developed to discern bimodality. A first step is therecognition of clustering of results into two populations. Whilerecognition of the separation point between the two populations iscommonly done by visual inspection of histograms of the data,statistical methods can be applied by one of ordinary skill in the artof statistics. The mean and standard deviation of the two clusters arecalculated. In a preferred definition of bimodality data are consideredbimodal if the means of the two clusters are equal or further apart thanthe sum of the standard deviations of the two clusters (Schilling,Watkins and Watkins, “Is Human Height Bimodal?”, The AmericanStatistician (2002), 56(3): 223-229).

Populations are regarded as dichotomous with respect to the microRNAresponses where a bimodal distribution amongst responses of the testcohort can be demonstrated for one or more microRNA. It is understoodthat not all patient cohorts are dichotomous with respect to theirresponse to the individual microRNA responses following any givenintervention. When a bimodal response is found in one or more microRNAs,then the patients populations are regarded as dichotomous.

In a preferred embodiment, an individual of ordinary skill in the artusing a human microRNA array from Agilant Technologies (for example cat.G4471A-029297) and following the directions of the manufacturer)quantifies all known human microRNAs on specimens RNA extracted fromPBMCs according to instructions of the microarray manufacturer. Testingis performed on specimens drawn preferably one to three weeks or lessprior to and one to three weeks or less following a therapeuticintervention.

In a preferred embodiment, a statistically significant number ofpatients are selected who have similar demographic and clinicalcharacteristics such as age, sex, and clinical condition, e.g. recurrentpregnancy loss. It is the goal of analysis of these data to segregatepatients into groups of two or more. In a more preferred embodiment, itis the goal to dichotomize patients into two groups each possessing aunique microRNA profile.

A variety of methods are suitable for determining unique microRNAprofiles defining each group. In a first step, the differences betweeneach microRNA sampled prior to therapy and subsequent to therapy arecalculated. The means and standard deviations of each of differencesbetween the various “before and after” sample subgroups are calculatedand microRNAs sorted in order of statistical significance. Subgroupswith microRNAs with the most statistically significant difference inmean and SD are selected (Graphpad software t test). By inspection oneidentifies a microRNA where the results are distributed dichotomously.Patient results are then sorted into two groups as determined by thegroup into which that selected microRNA falls.

As discussed below, a bimodal hsa-mir-132 response is shown amongst acohort of seventeen patients. The RNA before and after specimens frompatients from each the two cohorts defined by the bimodal distributionof hsa-mir-132 were subsequently assessed in a microarray of all “known”or suspected human microRNAs. As such, hsa-mir-132 is identified asmeeting the above stated criterion.

Seventeen female patients (average age 35.8±4.8 years) being seen at theAlan E. Beer Center for recurrent miscarriage and infertility and beingtreated with intravenous immunoglobulin (IVIG) (average 1.5±1.8 priormiscarriages; 1.6±1.5 prior IVF failures) were selected. Each patienthad signed a consent permitting their blood to be used for researchpurposes. Each patient selected for the study had a blood draw anaverage of 13.2±6.0 days prior to IVIG therapy and an average of11.8±5.6 days following IVIG therapy (an average of 25.1±7.9 daysbetween microRNA blood draws). Blood was drawn as a part of routineblood studies performed on patients. PBMCs were isolated fromtwenty-four to forty-eight hour old heparinized blood that had beenmaintained at room temperature by Ficoll-Hypaque density gradientcentrifugation. Approximately 10×10⁶ cells were preserved in one mlTrizol (Invitrogen) and maintained at −20° C. until used. Table 1summarizes the characteristics of the patients.

TABLE 1 Population parameters Mean ± SD Age (yrs) 35.8 ± 4.8 #priormiscarriages  1.5 ± 1.8 #prior IVF failures  1.6 ± 1.5 #days prior toIVIG therapy 13.2 ± 6.0 (Sample1) #days after IVIG therapy 11.8 ± 5.6(Sample2) #days between Sample 1 and 2 25.1 ± 7.9

The PBMCs from the seventeen patients were then interrogated for mir-16,mir-132, mir-146a, mir-155, mir-181a, mir-196a, mir-223 using RNU48 as ahousekeeping gene for purposes of normalization on reversed transcribedRNA by real-time PCR. Each patient had been treated with intravenousimmunoglobulin (IVIG) therapy and blood collected prior to and followingtherapy and the microRNAs quantified in each sample. Candidate microRNAswere selected from review of the literature. The microRNAs were selectedbased on the studies in autoimmune/inflammatory disorders (Lupus andrheumatoid arthritis on PBMCs. (mir16, mir-132, mir146a, mir155,mir181a, mir196a and mir223 were selected based information from thefollowing articles: (1) Pauley K M, Satoh M, Chan A L, Bubb M R, ReevesW H, Chan E K. Upregulated mir-146a expression in peripheral bloodmononuclear cells from rheumatoid arthritis patients. Arthritis ResTher. 2008; 10(4):R101.; (2) Dai Y, Huang Y S, Tang M, Lv T Y, Hu C X,Tan Y H, Xu Z M, Yin Y B. Microarray analysis of microRNA expression inperipheral blood cells of systemic lupus erythematosus patients. Lupus.2007; 16(12):939-46.; (3) Fehniger T A, Wylie T, Germino E, Leong J W,Magrini V J, Koul S, Keppel C R, Schneider S E, Koboldt D C, Sullivan RP, Heinz M E, Crosby S D, Nagarajan R, Ramsingh G, Link D C, Ley T J,Mardis E R. Next-generation sequencing identifies the natural killercell microRNA transcriptome. Genome Res. 2010 November;20(11):1590-604.)

Blood samples are collected in EDTA-treated tubes and PBMCs are isolatedby standard Ficoll density-gradient cenrifugation according to theprocedure known to those of ordinary skill in the art. Alternatively,the procedure utilizing Accupsin Tubes from Sigma-Aldrich following themanufacturer's procedure (procedure no AST-1) may be followed. PBMCs arewashed once in sterile phosphate-buffered saline (PBS) before RNAisolation. Total RNA is isolated from freshly obtained PBMCs using themir-Vana microRNA Isolation kit (Ambion, Austin, Tex., USA), inaccordance with the manufacturer's protocol. RNA concentrations aredetermined by absorption spectroscopy due to the peak absorption of DNAand RNA at 260 nm. 10 ng of each RNA sample are used for quantitativereal-time RT-PCR (qRT-PCR). microRNA qRT-PCR was performed using theTaqMan MicroRNA Reverse Transcription Kit, TaqMan Universal PCR MasterMix, and TaqMan MicroRNA Assay (Applied Biosystems, Foster City, Calif.,USA.) Primers from SABiosciences for these specific human microRNAs wereused: mir-16 (MPH00062A), mir-132 (MPH01167A), mir-146a (MPH00047A),mir-155 (MPH00059A), mir-223 (MPH01231A), let-7a (MPH00001A). mRNAqRT-PCR may be performed using the TaqMan High-Capacity cDNA ReverseTranscription Kit, TaqMan Fast PCR Master Mix, and TaqMan mRNA assayprimers (Applied Biosystems). Seehttp://www3.appliedbiosystems.com/cms/groups/mcb_support/documents/generaldocuments/cms_(—)042167.pdf.Reactions may be analyzed using StepOne Real-Time PCR System (AppliedBiosystems). The levels of microRNA is normalized to 18S RNA, forexample. The cycle threshold (Ct) values, corresponding to the PCR cyclenumber at which fluorescence emission reaches a threshold above baselineemission, were determined and the relative microRNA or mRNA expressionwas calculated using the 2-ΔΔCt method (Applied Biosystems User BulletinNo. 2)

Based on the literature, a coordinate change in microRNAs could beexpected. Moreover, changes in the range of several fold between sampleswould also be expected. For example, changes in mir-146a might beexpected from the above Pauley reference. Changes in mir-16 could alsobe anticipated. However, unexpectedly, a single microRNA, mir-132 wassuppressed up to approximately 100-fold. IVIG therapy appears tosuppress the expression of mir-132 very specifically and very markedly.Further, initial values varied significantly between patients by up toapproximately 100 fold. Table 2 shows that amongst 17 patients in whombefore and after studies were performed, IVIG treatment resulted in astatistically significant decrease.

TABLE 2 Mean mir-132 CT levels 17 patient sequential cases (mean ± SD)Before IVIG 22.8 ± 4.7* After IVIG 26.7 ± 2.1* Difference +3.9 ± 4.0  *p= 0.004

As discussed herein, the treatment group may be divided into two groupson the basis of the expression pattern one microRNA, mir-132, prior toIVIG therapy. Group A had low initial CTs (threshold crossing)indicating relatively high initial levels of hsa-mir-132. The remainingpatients, Group B, had high initial CTs indicating low levels ofhsa-mir-132. Following IVIG treatment, both groups converged in theirlevels of hsa-mir-132 to high CTs indicating significantly diminishedlevels of mir-132. The changes were substantially greater in Group Athan in Group B. IVIG appears to have been most effective in loweringmir-132 in the group that had the highest levels of mir-132pretreatment.

Although both groups responded statistically to IVIG treatment, Table 3dichotomizes patients by their initial hsa-mir-132 CT. It can be seenthat the initial CTs cluster into those with relatively low CTs (highconcentrations of hsa-mir-132) and those with relatively high CTs (lowlevels of hsa-mir-132). Following IVIG therapy, the two groups convergesuch that the CT change in the low CT group is significantly greaterthan that of the high CT group. Accordingly, an embodiment of theinvention is the separation of the patients into two distinct groups(dichotomization) on the basis of microRNA expression.

TABLE 3 Low mir-132 group: 6 High mir-132 cases group: 11 cases(Sample1 >21.0) (Sample 1 <19.0) Mean initial mir-132 value 17.1 ± 0.825.9 ± 2.2 (Sample 1) Mean post-IVIG mir-132 25.7 ± 1.2 27.4 ± 2.2 value(Sample 2) Mean microRNA change  +8.5 ± 1.2**  +1.5 ± 2.4** **p < 0.0001

These results are represented in FIGS. 1 and 2, which show the CT levelsof the patients before and after IVIG treatment, for Groups A and Brespectively. As shown in FIG. 1, prior to IVIG treatment, Group Aexhibited CT levels of 16-18 and exhibited levels of 24-27 aftertreatment. On the other hand, Group B exhibited levels of 21-29 beforetreatment and levels of 23-30 after treatment.

Those patients with initially very low levels had modest to very smalldegrees of suppression. Following IVIG therapy, mir-132 values from boththe patients groups appeared to converge at relatively the same lowmicroRNA activity (or high CT) level (CT value between 23-28). Itappears that the effect of IVIG on mir-132 expression is largelyconfined to those patients with initially relatively large quantities ofmir-132 (those with low CT levels). This population has an initialmir-132 CT range between 16-18. Thus IVIG therapy is most effective insuppression of mir-132 in the group of patients with the highestpre-treatment levels of mir-132.

Using hsa-mir-132 to identify patients belonging to two discrete groupsas described above, four patients from each of Group A and Group B werethen assessed by an Agilant microarray comprising all of the known humanmicroRNAs (approximately 900 microRNAs).

Data from each of the two groups were assessed separately and each groupsorted by the mean differences between the first and secondquantification of the specific microRNA for each group separately andlisted in order of decreasing differences. From the said lists, thedifferences that are most increased in a first group are compared to themost decreased in a second group. Conversely the most decreased in afirst group are compared to the most increased in a second group. Thoseindividual microRNAs that display the greatest differences between themeans of the differences in each group are considered to beprovisionally optimum markers. The power of these optimum microRNAs arefurther assessed by the sum of the standard deviations of the means ofthe respective groups to confirm their status as optimum markers whereinthe ratio of the differences is divided by the sum of the standarddeviations. Optimum markers have the highest ratios. For example, all ofthe microRNAs listed below in Tables 4-6 exhibit ratios with an absolutevalue greater than or equal to one. More preferably, microRNAs may beselected that exhibit a ratio with an absolute value greater than orequal to two.

The data in Table 4 identifies useful microRNA markers meeting the abovecriteria

TABLE 4 Group A Selected Mean Group B MicroRNA difference Group A Meandifference Group B markers after IVIG Mean SD after IVIG Mean SDhsa-mir- 2.52122 1.497334 −5.43651 0.82098 136 hsa-mir- 1.0837030.288785 −4.77102 0.469596 141 hsa-mir- 1.041446 0.142776 −5.944620.44929 142-5p hsa-mir- 0.335061 1.466308 −5.88934 1.274726 144 hsa-mir-2.082454 0.849342 −7.84218 0.691729 153 hsa-mir- 1.301357 0.995047−7.47155 1.143166 1537 hsa-mir- 0.952666 0.441789 −4.08226 0.877131193a-3p hsa-mir- 2.083515 1.609553 −6.60915 0.991381 219-5p hsa-mir-1.068173 0.318097 −5.26843 0.506796 29b hsa-mir- 1.140525 0.150147−4.74399 0.497264 301a hsa-mir-32 1.757013 0.663302 −6.60811 0.775835hsa-mir- 2.208455 0.541141 −6.66695 0.528549 33a hsa-mir- 1.9555941.558846 −6.95672 0.388828 545 hsa-mir- 0.885343 0.493095 −5.632841.148245 582-3p hsa-mir- 1.195371 0.219509 −5.34069 0.410219 590-5phsa-mir- −1.82126 1.984385 4.492508 1.085892 1181 hsa-mir- −0.902531.698756 8.720988 1.695736 513a-5p

As can be seen from inspection of the listed microRNAs from each of thetwo groups, two distinct observations can be made. First, the valuesmoved in opposite directions following IVIG therapy in the two groups.For example, hsa-mir-136 increases following IVIG while it decreases ingroup B. Conversely, hsa-mir-513-5p decreases in group A while itincreases in group B. Second, taken as a single group, the standarddeviations are quite broad while the standard deviations within eachgroup, A or B, are greatly diminished. These findings confirm the 1recognition of two distinct groupings of patients. From a singlemicroRNA result for a patient, one can assign the patient to one of thetwo groups statistically. One of ordinary skill in the art can assignpatients to the appropriate group.

Additional sequences have been identified that may be used separately orin combination to define members of group A or B are selected on thebasis of demonstrating opposite behavior after IVIG. Table 5 listscommon microRNAs selected from the top 100 most increased in Group A and100 most decreased in Group B while Table 6 lists common microRNAsselected from the top 100 most decreased in Group A and 100 mostincreased in Group B.

TABLE 5 Group A Group B Mean Group A Mean Group B MicroRNA Difference SDDifference SD hsa-mir-1 1.270745 1.003145 −3.44195 2.957159 hsa-mir-1010.698965 0.235172 −4.10335 0.423924 hsa-mir- 0.332102 2.511271 −7.189122.240812 1183 hsa-mir- 0.511253 0.559915 −2.90416 0.69329 1249hsa-mir-136 2.52122 1.497334 −5.43651 0.82098 hsa-mir- 0.419807 0.265255−2.03009 0.349193 140-5p hsa-mir-141 1.083703 0.288785 −4.77102 0.469596hsa-mir- 1.0677 0.202879 −3.59343 0.583092 142-3p hsa-mir- 1.0414460.142776 −5.94462 0.44929 142-5p hsa-mir-144 0.335061 1.466308 −5.889341.274726 hsa-mir-153 2.082454 0.849342 −7.84218 0.691729 hsa-mir-1.301357 0.995047 −7.47155 1.143166 1537 hsa-mir-15a 0.533715 0.100281−2.8992 0.322076 hsa-mir-18a 0.382843 0.13646 −1.92863 0.464262hsa-mir-18b 0.405855 0.187818 −2.19576 0.531698 hsa-mir- 0.9526660.441789 −4.08226 0.877131 193a-3p hsa-mir-19a 0.872497 0.133295 −4.33930.511408 hsa-mir-19b 0.457604 0.158196 −2.57291 0.322324 hsa-mir-210.441811 0.278029 −2.1423 0.382552 hsa-mir- 2.083515 1.609553 −6.609150.991381 219-5p hsa-mir-27a 0.53133 0.25451 −2.12834 0.263011hsa-mir-29b 1.068173 0.318097 −5.26843 0.506796 hsa-mir-29c 0.4433750.117018 −2.75245 0.365226 hsa-mir- 1.140525 0.150147 −4.74399 0.497264301a hsa-mir- 0.976275 0.357315 −3.26383 0.442348 301b hsa-mir-30e0.59185 0.144703 −3.24738 0.251854 hsa-mir-32 1.757013 0.663302 −6.608110.775835 hsa-mir- 0.698976 0.359357 −1.87021 0.15051 324-5p hsa-mir-3350.617394 0.711001 −2.4438 0.722963 hsa-mir- 1.10453 1.617308 −4.147232.179625 337-5p hsa-mir- 0.391677 0.0348 −3.00793 0.498721 338-3phsa-mir-33a 2.208455 0.541141 −6.66695 0.528549 hsa-mir-340 0.8306690.481914 −4.16497 0.300849 hsa-mir- 0.8536 0.294971 −3.28599 0.665745362-3p hsa-mir- 0.971021 1.083568 −2.46387 1.359333 371-5p hsa-mir-0.46162 1.952296 −6.23567 1.991346 376b hsa-mir- 0.363715 0.19741−2.06665 0.272203 374a hsa-mir-377 0.873289 1.253133 −1.92671 1.06699hsa-mir-421 0.824237 1.119436 −2.52667 0.979143 hsa-mir-424 0.6764290.131418 −3.30699 0.509319 hsa-mir- 0.835738 0.202614 −5.38285 0.608003542-3p hsa-mir-545 1.955594 1.558846 −6.95672 0.388828 hsa-mir- 0.7103250.382632 −1.977 0.399167 548c-5p hsa-mir- 1.132591 0.828951 −2.307010.520072 551b hsa-mir- 0.885343 0.493095 −5.63284 1.148245 582-3phsa-mir- 1.195371 0.219509 −5.34069 0.410219 590-5p

TABLE 6 Group A Mean Group A Group B Mean Group B MicroRNA difference SDdifference SD hsa-mir-100 −0.75803 1.061571 1.154144 0.766084hsa-mir-1181 −1.82126 1.984385 4.492508 1.085892 hsa-mir-1227 −0.361350.981072 3.887691 0.570974 hsa-mir-1271 −0.49835 0.575051 1.2519070.418329 hsa-mir-127-3p −1.07828 1.295253 1.312743 0.450348 hsa-mir-1275−0.47159 0.576344 0.968348 0.519534 hsa-mir- −1.27118 1.97 2.7688530.297402 1300_v13.0 hsa-mir-1307 −0.41137 1.091938 5.269371 0.913262hsa-mir-139-3p −0.37258 0.494547 2.047472 0.768133 hsa-mir-181a-2*−0.39928 0.430931 2.779431 0.677482 hsa-mir-182 −0.53534 0.7458663.998238 0.721076 hsa-mir-191 −0.60784 0.657613 3.506353 0.577494hsa-mir-224 −0.5147 1.993012 2.390506 1.670223 hsa-mir-300 −1.481211.881716 2.084424 1.659764 hsa-mir-339-5p −1.7283 1.326469 1.4053270.459023 hsa-mir-483-3p −0.58871 2.243166 2.07591 0.843905hsa-mir-486-5p −1.20003 1.525774 1.160433 1.15879 hsa-mir-501-5p−0.48667 1.118063 4.393115 0.758058 hsa-mir-513a-5p −0.90253 1.6987568.720988 1.695736 hsa-mir-564 −0.38684 0.709046 1.351019 0.832139hsa-mir-602 −0.45617 0.664475 1.969851 0.830315 hsa-mir-630 −1.207693.331194 0.968519 2.660711 hsa-mir-647 −0.84299 1.117714 4.4833331.436393 hsa-mir-770-5p −0.57186 1.473769 1.18536 0.33416 hsa-mir-885-5p−0.47354 3.442087 3.238278 1.381079 hsa-mir-892b −0.48425 1.4870332.127233 0.882215 hsa-mir-92b −0.5896 0.830008 5.23262 0.53149

Identification of the dichotomized groups can be determined from asingle microRNA assay performed. As can be seen from above, hsa-mir-132discretely separates the A and B groups. Additional sequences tabulatedin table 6 provide additional sequences that may be used to define thetwo groupings. They may be used singly or in combination. Thedichotomous nature of a population can be ascertained from selection ofmicroRNAs that display large standard deviation. These microRNAs arelikely to comprise a bimodal distribution of microRNA means. Inspectionor application of statistical tests by one of ordinary skill in the artof statistics can verify bimodality.

To test the hypothesis that patients can be divided into dichotomousgroups wherein individual microRNAs can be identified that responddifferently to a therapeutic intervention, a sham grouping of the samepatients wherein dichotomous groupings demonstrated differentialmicroRNA response as seen in tables 4-6, were constructed. In the aboveexamples, patients were designated as group A or B according to theresponse of hsa-mir-132 following IVIG therapy. Each group comprisedfour patients. In the sham experiment, two patients were arbitrarilyselected from Group A and two from Group B to comprise a sham group Cwhile the remaining two patients from group A and the two remainingpatients from group B comprise a sham group D. The differences in themeans and standard deviations before and after therapy were calculatedand displayed as the 30 most increased following therapy and the 30 mostdecreased as shown in Table 7.

TABLE 7 Mean Δ Mean Δ MicroRNA Group C SD MicroRNA Group D SDhsa-miR-24-1* 1.139664 1.751306 hsa-miR-34b* −0.495378 0.661462hsa-miR-513b 1.10647 1.745331 hsa-miR-1207- −0.499623 0.345578 5phsa-miR-513a-5p 1.081663 2.387287 hsa-miR-1274a −0.507595 0.863872hsa-miR-513c 1.006773 1.331825 hsa-miR-1280 −0.516564 0.713269hsa-miR-345 0.873768 1.69969 hsa-miR-28-3p −0.525916 0.890524hsa-miR-892b 0.823898 0.561379 hsa-miR-181a* −0.526303 0.449876 hsa-miR-0.753516 0.90899 hsa-miR-1226* −0.537768 0.995808 1826_v15.0hsa-miR-501-5p 0.721953 1.183809 hsa-miR-582-5p −0.538055 0.930185hsa-miR-760 0.720185 1.313588 hsa-miR-500a −0.539322 0.799103hsa-miR-92b 0.719088 0.986911 hsa-miR-133a −0.553997 1.031349hsa-miR-23a* 0.711232 0.624211 hsa-miR-720 −0.570416 0.810315hsa-miR-607 0.694857 0.669177 hsa-miR-1260 −0.575785 0.947614hsa-miR-338-5p 0.694838 0.66914 hsa-miR-135a* −0.577329 1.032842hsa-miR-665 0.657335 0.594854 hsa-miR-92a-1* −0.595985 0.496119hsa-miR-576-3p 0.640433 0.562254 hsa-miR-1202 −0.62187 0.826414hsa-miR-574-3p 0.636028 1.161566 hsa-miR-194* −0.632359 1.510496miRNABrightCorner30 0.619267 0.43915 hsa-miR-629* −0.635601 1.061238hsa-miR-494 0.574397 0.384558 hsa-miR-26b* −0.66143 0.511278hsa-miR-1288 0.572928 0.526309 hsa-miR-192* −0.672978 1.370112hsa-miR-378 0.508068 0.394278 Has-miR-630 −0.679997 2.042544hsa-miR-139-3p 0.463569 0.641463 hsa-miR-183 −0.687276 2.202468hsa-let-7a* 0.448805 0.297109 hsa-miR-373* −0.707562 1.093235hsa-let-7c* 0.448805 0.297109 hsa-miR-1181 −0.727918 2.188832hsa-let-7e* 0.448805 0.297109 hsa-miR-557 −0.759721 0.679964hsa-let-7f-2* 0.448805 0.297109 hsa-miR-134 −0.761638 0.814211hsa-let-7g* 0.448805 0.297109 hsa-miR-892b −0.78585 0.907683hsa-miR-100* 0.448805 0.297109 hsa-miR-18b* −0.803171 1.135087hsa-miR-103-as 0.448805 0.297109 hsa-miR-188-5p −0.885158 1.808363hsa-miR-105 0.448805 0.297109 hsa-miR-760 −0.901819 1.305335hsa-miR-105* 0.448805 0.297109 hsa-miR-150* −0.93806 0.866707

Not only were the differences in means between the two sham groupsrelatively small, but the standard deviations were sufficiently largesuch that no microRNA was identified that could be considered bimodal.In other words, the absolute value of the differences in means dividedby the sum of the standard deviations was less than one for eachmicroRNA using this random grouping. Therefore a random“dichotomization” does not identify separate patient groups as definedin this disclosure.

To predict assignment of patients to a response group, in a sampleacquired before or after an intervention, microRNAs are sorted by themaximum difference between microRNA levels between the two groupsdefined above. The power of these microRNA markers is further assessedby the ratio of the differences in means of the microRNA values dividedby the sum of their standard deviations. Tables 8 and 9 comprise aselective list and a more comprehensive list of such microRNAs,respectively, with the means and standard deviations of expressionlevels before IVIG treatment.

TABLE 8 Ratio from calculation microRNA 5.120617 hsa-miR-548d-5p4.969583 hsa-miR-548a-5p 3.438886 hsa-miR-1537 2.615145 hsa-miR-590-5p2.558528 hsa-miR-33a 2.547821 hsa-let-7e 2.480264 hsa-miR-32 2.330219hsa-miR-301a 2.27536 hsa-miR-30e 2.22848 hsa-miR-19a 2.151041hsa-miR-142-5p 2.144081 hsa-miR-362-3p 2.10237 hsa-miR-301b 2.052236hsa-miR-1183 2.051132 hsa-miR-142-3p 2.027244 hsa-miR-340 1.977995hsa-miR-371-5p −1.98464 hsa-miR-154 −2.11305 hsa-miR-423-3p −2.1648hsa-miR-1224-5p −2.19 hsa-miR-191 −2.19444 hsa-miR-127-3p −2.24513hsa-miR-574-5p −2.4263 hsa-miR-139-3p −2.7148 hsa-miR-432

TABLE 9 Ratio from calculation microRNA 5.120617 hsa-miR-548d-5p4.969583 hsa-miR-548a-5p 3.438886 hsa-miR-1537 2.615145 hsa-miR-590-5p2.558528 hsa-miR-33a 2.547821 hsa-let-7e 2.480264 hsa-miR-32 2.330219hsa-miR-301a 2.27536 hsa-miR-30e 2.22848 hsa-miR-19a 2.151041hsa-miR-142-5p 2.144081 hsa-miR-362-3p 2.10237 hsa-miR-301b 2.052236hsa-miR-1183 2.051132 hsa-miR-142-3p 2.027244 hsa-miR-340 1.977995hsa-miR-371-5p 1.861578 hsa-miR-15a 1.857642 hsa-miR-548c-5p 1.850123hsa-miR-1225-3p 1.837303 hsa-miR-29b 1.834596 hsa-miR-21 1.820351hsa-miR-1237 1.813523 hsa-miR-101 1.802173 hsa-miR-1539 −1.38055hsa-miR-602 −1.38147 hsa-miR-132 −1.44539 hsa-miR-1471 −1.45911hsa-miR-495 −1.59946 hsa-miR-1181 −1.60944 hsa-miR-339-5p −1.62878hsa-miR-134 −1.6329 hsa-miR-183 −1.72047 hsa-miR-557 −1.81989hsa-miR-125a-3p −1.83853 hsa-miR-423-5p −1.94998 hsa-miR-382 −1.98464hsa-miR-154 −2.11305 hsa-miR-423-3p −2.1648 hsa-miR-1224-5p −2.19hsa-miR-191 −2.19444 hsa-miR-127-3p −2.24513 hsa-miR-574-5p −2.4263hsa-miR-139-3p −2.7148 hsa-miR-432

Correspondingly, Tables 10 and 11 comprise a selective list and a morecomprehensive list of such microRNAs, respectively, representingsignificant ratios in patients after IVIG treatment.

TABLE 10 Ratio from calculation microRNA 2.883848 hsa-miR-125a-5p2.589854 hsa-miR-92b 2.108284 hsa-let-7e 2.083486 hsa-miR-1307 −2.13615hsa-miR-376b −2.25541 hsa-miR-29b −2.28617 hsa-miR-543 −2.29295hsa-miR-301a −2.35023 hsa-miR-15a −2.37193 hsa-miR-1249 −2.40239hsa-miR-542-3p −2.43732 hsa-miR-136 −2.44794 hsa-miR-140-5p −2.5479hsa-miR-32 −2.56767 hsa-miR-33a −2.58454 hsa-miR-545 −2.75021hsa-miR-340 −2.7781 hsa-miR-590-5p −2.80882 kshv-miR-K12-10b −2.82348hsa-miR-142-5p −3.0019 hsa-miR-923_v12.0 −3.02147 hsa-miR-101 −3.20361hsa-miR-19a −3.88505 hsa-miR-141 −4.08184 hsa-miR-548c-5p −4.53511hsa-miR-30e −5.1227 hsa-miR-153

TABLE 11 Ratio from calculation microRNA 2.883848 hsa-miR-125a-5p2.589854 hsa-miR-92b 2.130259 miRNABrightCorner30 2.108284 hsa-let-7e2.083486 hsa-miR-1307 2.073077 hsa-miR-886-3p_v15.0 1.956005 hsa-miR-2221.860369 mr_1 1.798634 hsa-miR-338-5p 1.793474 hsa-miR-664 1.781682hsa-miR-1227 1.717443 hsa-miR-99b 1.676623 hsa-miR-363 −1.6569hsa-miR-377 −1.68107 hsa-miR-450a −1.72193 hsa-miR-376c −1.76059hsa-miR-382 −1.78329 hsa-miR-1537 −1.82416 hsa-miR-29c −1.8549hsa-miR-144 −1.85679 hsa-miR-19b −1.88001 hsa-miR-106b −1.88841hsa-miR-499-5p −1.90758 hsa-miR-376a −1.96386 hsa-miR-362-3p −1.99976hsa-miR-154 −2.035 hsa-miR-337-5p −2.04325 hsa-miR-424 −2.06191hsa-miR-219-5p −2.13615 hsa-miR-376b −2.25541 hsa-miR-29b −2.28617hsa-miR-543 −2.29295 hsa-miR-301a −2.35023 hsa-miR-15a −2.37193hsa-miR-1249 −2.40239 hsa-miR-542-3p −2.43732 hsa-miR-136 −2.44794hsa-miR-140-5p −2.5479 hsa-miR-32 −2.56767 hsa-miR-33a −2.58454hsa-miR-545 −2.75021 hsa-miR-340 −2.7781 hsa-miR-590-5p −2.80882kshv-miR-K12-10b −2.82348 hsa-miR-142-5p −3.0019 hsa-miR-923_v12.0−3.02147 hsa-miR-101 −3.20361 hsa-miR-19a −3.88505 hsa-miR-141 −4.08184hsa-miR-548c-5p −4.53511 hsa-miR-30e −5.1227 hsa-miR-153

To assess to ability of microRNAs to predict microRNA response totherapy, microRNAs that function similarly upon treatment between thedichotomized groups, microRNAs that either increase together or decreasetogether were collected and listed in Table 12.

TABLE 12 Mean Δ Mean Δ Net Δ MicroRNA Group A SD Group B SD A and Bhsa-mir-1470 down −0.7673864 1.235473 −4.12664194 1.5502503 down−3.35926 hsa-mir-1290 down −0.0147208 1.1018548 −3.154401463 1.1577212down −3.13968 hsa-mir-1202 down −0.3401371 0.8489831 −3.37430041.1013233 down −3.03416 hsa-mir-212 down −0.694552 0.7209563 −3.70403520.89993 down −3.00948 hsa-mir-26b down −0.0189347 0.3277201 −1.4864390.2695336 down −1.4675 hsa-mir-623 up 0.5154214 0.3045152 3.28963021.1049068 up 2.774209 hsa-mir- up 0.0226423 0.5811046 2.889622711.1343319 up 2.86698 1826_v15.0 hsa-mir-574- up 0.2439299 1.54617133.300805 0.7679533 up 3.056875 3p hsa-mir-1471 up 0.6108116 2.91570164.0440545 1.3350063 up 3.433243 hsa-mir-337- up 0.2684066 1.41405224.8525722 0.9392156 up 4.584166 3p hsa-mir-513b up 0.6818242 1.38464475.8524707 1.7107123 up 5.170647

Certain patterns of response may indicate resistance or responsivenessof one the two groups. While there may be a significant change in valueof one or more microRNAs in one group there may be little response inthe other. The group exhibiting lesser change may indicate a lack ofresponse to the therapeutic intervention. This group may be resistant tothe intervention or that the intended therapeutic effect is not needed.The response of hsa-mir-132 is exemplary. Group A patients hadrelatively low CTs by PCR (relatively high concentrations of themicroRNA) while group B patients had relatively high CTs. A briskresponse was noted following therapy in group A patients substantiallyconverging on the low levels noted in group B patients. The possibilitythat the therapeutic effect on the levels of the microRNA in group Bwere already at a level that the therapeutic intervention was capable ofeffecting. The following tables demonstrate subsets of microRNAsexhibiting relatively significant change in one group and reduced changein the other group. Further, it can be seen that the group exhibiting asignificant change may either converge to a level of expressioncorresponding to the other group or diverge. Specifically, Tables 13 and14 are selective lists of microRNAs having divergent and convergentbehavior, respectively, which exhibit significant change in Group A butrelatively little change in Group B.

TABLE 13 Mean Δ Group A SD SystematicName Direction 2.52122 1.497334hsa-miR-136 Most increased 2.208455 0.541141 hsa-miR-33a Most increased2.083515 1.609553 hsa-miR-219-5p Most increased 2.082454 0.849342hsa-miR-153 Most increased 1.955594 1.558846 hsa-miR-545 Most increased1.757013 0.663302 hsa-miR-32 Most increased 1.301357 0.995047hsa-miR-1537 Most increased 1.195371 0.219509 hsa-miR-590-5p Mostincreased −1.82126 1.984385 hsa-miR-1181 Most decreased

TABLE 14 Mean Δ Group A SD SystematicName Direction 1.270745 1.003145hsa-miR-1 Most increased 0.933754 1.321562 hsa-miR-376a Most increased−1.20769 3.331194 hsa-miR-630 Most decreased −1.26517 0.328726hsa-miR-886-3p_v15.0 Most decreased −1.27118 1.97 hsa-miR-1300_v13.0Most decreased −1.29486 3.144593 hsa-miR-485-3p Most decreased −1.321611.728257 hsa-miR-1224-5p Most decreased −1.48121 1.881716 hsa-miR-300Most decreased −1.50279 0.935894 hsa-miR-132 Most decreased −1.72831.326469 hsa-miR-339-5p Most decreased −1.74399 3.594059 kshv-miR-K12-9Most decreased

Likewise, Tables 15 and 16 are more comprehensive lists of microRNAshaving divergent and convergent behavior, respectively, which exhibitsignificant change in Group A but relatively little change in Group B.

TABLE 15 Mean Δ Group A SD SystematicName Direction 2.52122 1.497334hsa-miR-136 Most increased 2.208455 0.541141 hsa-miR-33a Most increased2.083515 1.609553 hsa-miR-219-5p Most increased 2.082454 0.849342hsa-miR-153 Most increased 1.955594 1.558846 hsa-miR-545 Most increased1.757013 0.663302 hsa-miR-32 Most increased 1.301357 0.995047hsa-miR-1537 Most increased 1.195371 0.219509 hsa-miR-590-5p Mostincreased 1.140525 0.150147 hsa-miR-301a Most increased 1.0837030.288785 hsa-miR-141 Most increased 1.068173 0.318097 hsa-miR-29b Mostincreased 1.041446 0.142776 hsa-miR-142-5p Most increased 0.9526660.441789 hsa-miR-193a-3p Most increased 0.885343 0.493095 hsa-miR-582-3pMost increased −0.90253 1.698756 hsa-miR-513a-5p Most decreased −1.821261.984385 hsa-miR-1181 Most decreased

TABLE 16 Mean Δ Group A SD SystematicName Direction 1.270745 1.003145hsa-miR-1 Most increased 1.13486 1.316732 hsa-miR-376c Most increased1.132591 0.828951 hsa-miR-551b Most increased 1.10453 1.617308hsa-miR-337-5p Most increased 1.0677 0.202879 hsa-miR-142-3p Mostincreased 1.049269 1.235692 hsv1-miR-H1_v14.0 Most increased 1.0353472.190203 hsa-miR-410 Most increased 0.976275 0.357315 hsa-miR-301b Mostincreased 0.971021 1.083568 hsa-miR-371-5p Most increased 0.9337541.321562 hsa-miR-376a Most increased 0.88329 0.745268 hsa-miR-133b Mostincreased −0.75803 1.061571 hsa-miR-100 Most decreased −0.76739 1.235473hsa-miR-1470 Most decreased −0.80433 0.948616 hsa-miR-1260 Mostdecreased −0.84299 1.117714 hsa-miR-647 Most decreased −0.8683 2.617779hsa-miR-595 Most decreased −0.87714 1.87364 hsa-miR-525-5p Mostdecreased −0.88588 2.114378 hsa-miR-188-5p Most decreased −0.947060.701991 hsa-miR-28-3p Most decreased −0.99436 2.03129 hsa-miR-96 Mostdecreased −1.02709 1.633777 hsa-miR-451 Most decreased −1.07284 0.683653hsa-miR-134 Most decreased −1.07828 1.295253 hsa-miR-127-3p Mostdecreased −1.08028 2.11365 hsa-miR-663 Most decreased −1.20003 1.525774hsa-miR-486-5p Most decreased −1.20769 3.331194 hsa-miR-630 Mostdecreased −1.26517 0.328726 hsa-miR-886-3p_v15.0 Most decreased −1.271181.97 hsa-miR-1300_v13.0 Most decreased −1.29486 3.144593 hsa-miR-485-3pMost decreased −1.32161 1.728257 hsa-miR-1224-5p Most decreased −1.481211.881716 hsa-miR-300 Most decreased −1.50279 0.935894 hsa-miR-132 Mostdecreased −1.7283 1.326469 hsa-miR-339-5p Most decreased −1.743993.594059 kshv-miR-K12-9 Most decreased

Correspondingly, Tables 17 and 18 are a selective list of microRNAshaving divergent and convergent behavior, respectively, which exhibitsignificant change in Group B and relatively little change in Group A.

TABLE 17 Mean Δ Group B SD SystematicName Direction 8.720988 1.695736hsa-miR-513a-5p Most increased 4.492508 1.085892 hsa-miR-1181 Mostincreased −5.88934 1.274726 hsa-miR-144 Most decreased −5.94462 0.44929hsa-miR-142-5p Most decreased −6.60811 0.775835 hsa-miR-32 Mostdecreased −6.60915 0.991381 hsa-miR-219-5p Most decreased −6.666950.528549 hsa-miR-33a Most decreased −6.95672 0.388828 hsa-miR-545 Mostdecreased −7.47155 1.143166 hsa-miR-1537 Most decreased −7.842180.691729 hsa-miR-153 Most decreased

TABLE 18 Mean Δ Group B SD SystematicName Direction 6.045644 1.810638hsa-miR-1296 Most increased 5.852471 1.710712 hsa-miR-513b Mostincreased 5.269371 0.913262 hsa-miR-1307 Most increased 5.23262 0.53149hsa-miR-92b Most increased 4.852572 0.939216 hsa-miR-337-3p Mostincreased 4.483333 1.436393 hsa-miR-647 Most increased 4.446777 2.354756hsa-miR-345 Most increased 4.393115 0.758058 hsa-miR-501-5p Mostincreased −6.23567 1.991346 hsa-miR-376b Most decreased −7.189122.240812 hsa-miR-1183 Most decreased

Finally, Tables 19 and 20 are more comprehensive lists of microRNAshaving divergent and convergent behavior, respectively, which exhibitsignificant change in Group B but relatively little change in Group A

TABLE 19 Mean Δ Group B SD SystematicName Direction 8.720988 1.695736hsa-miR-513a-5p Most increased 4.492508 1.085892 hsa-miR-1181 Mostincreased −4.08226 0.877131 hsa-miR-193a-3p Most decreased −4.743990.497264 hsa-miR-301a Most decreased −4.77102 0.469596 hsa-miR-141 Mostdecreased −5.26843 0.506796 hsa-miR-29b Most decreased −5.34069 0.410219hsa-miR-590-5p Most decreased −5.43651 0.82098 hsa-miR-136 Mostdecreased −5.63284 1.148245 hsa-miR-582-3p Most decreased −5.889341.274726 hsa-miR-144 Most decreased −5.94462 0.44929 hsa-miR-142-5p Mostdecreased −6.60811 0.775835 hsa-miR-32 Most decreased −6.60915 0.991381hsa-miR-219-5p Most decreased −6.66695 0.528549 hsa-miR-33a Mostdecreased −6.95672 0.388828 hsa-miR-545 Most decreased −7.47155 1.143166hsa-miR-1537 Most decreased −7.84218 0.691729 hsa-miR-153 Most decreased

TABLE 20 Mean Δ Group B SD SystematicName Direction 6.045644 1.810638hsa-miR-1296 Most increased 5.852471 1.710712 hsa-miR-513b Mostincreased 5.269371 0.913262 hsa-miR-1307 Most increased 5.23262 0.53149hsa-miR-92b Most increased 4.852572 0.939216 hsa-miR-337-3p Mostincreased 4.483333 1.436393 hsa-miR-647 Most increased 4.446777 2.354756hsa-miR-345 Most increased 4.393115 0.758058 hsa-miR-501-5p Mostincreased 4.139791 1.284721 hsa-miR-765 Most increased 4.10815 2.241581hsa-miR-299-5p Most increased 4.044055 1.335006 hsa-miR-1471 Mostincreased 3.998238 0.721076 hsa-miR-182 Most increased 3.887691 0.570974hsa-miR-1227 Most increased 3.841755 0.995814 hsa-miR-34b Most increased3.622937 1.26476 hsa-miR-491-3p Most increased 3.506353 0.577494hsa-miR-191 Most increased 3.399269 1.692025 hsa-miR-513c Most increased3.300805 0.767953 hsa-miR-574-3p Most increased 3.28963 1.104907hsa-miR-623 Most increased 3.238278 1.381079 hsa-miR-885-5p Mostincreased 3.047651 0.813464 hsa-miR-338-5p Most increased 2.9611041.572459 hsa-miR-609 Most increased 2.889623 1.134332 hsa-miR-1826_v15.0Most increased −4.09785 0.929085 kshv-miR-K12-10b Most decreased−4.10335 0.423924 hsa-miR-101 Most decreased −4.12664 1.55025hsa-miR-1470 Most decreased −4.14723 2.179625 hsa-miR-337-5p Mostdecreased −4.16497 0.300849 hsa-miR-340 Most decreased −4.3393 0.511408hsa-miR-19a Most decreased −4.3514 0.995718 hsa-miR-487a Most decreased−5.38285 0.608003 hsa-miR-542-3p Most decreased −6.23567 1.991346hsa-miR-376b Most decreased −7.18912 2.240812 hsa-miR-1183 Mostdecreased

The tables reproduced above represent a number of suitable techniquesfor classifying microRNAs that exhibit dichotomous behavior in thegroups of patients. In turn, it can be expected that such microRNAs maybe used to segregate patient populations and to assign individualpatients to appropriate groups for the purpose of diagnosis, treatmentor the like. Further, one of skill in the art will recognize thatcertain microRNAs stand out as being identified in a number of thetables. Accordingly, in a presently preferred embodiment, the techniquesof this disclosure can be practiced using one or more microRNAs selectedfrom the following group:

hsa-let-7e, hsa-mir-1181, hsa-miR-1183, hsa-miR-1224-5p, hsa-miR-127-3p,hsa-mir-1296, hsa-mir-132, hsa-mir-136, hsa-miR-139-3p, hsa-mir-141,hsa-miR-142-3p, hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537,hsa-miR-154, hsa-miR-191, hsa-mir-193a-3p, hsa-miR-19a, hsa-mir-219-5p,hsa-mir-29b, hsa-mir-301a, hsa-miR-301b, hsa-miR-30e, hsa-mir-32,hsa-mir-33a, hsa-miR-340, hsa-miR-362-3p, hsa-miR-371-5p, has-377,hsa-miR-423-3p, hsa-miR-432, hsa-mir-513a-5p, hsa-mir-545,hsa-miR-548a-5p, hsa-miR-574-5p, hsa-mir-582-3p, hsa-mir-590-5p,hsa-mir-15a, hsa-mir-548c-5p, hsa-mir-1225-3p, hsa-mir-29b, hsa-mir-21,hsa-mir-1237, hsa-mir-101, hsa-mir-1539, hsa-mir-557, hsa-mir-125a-3pand hsa-mir-423-5p

Even more preferably, Table 21 provides a list of 19 selected microRNAshaving been selected on the basis of one or more of the indicatedcriteria.

TABLE 21 Known Top 17 Uni- Before After AB bidirectional directionalIVIG IVIG divergent/ (A&B) mover A or B A or B convergent movers (A orB) predictor predictor behavior hsa-mir-136 x x x x hsa-mir-141 x xhsa-mir-142-5p x x x hsa-mir-144 x hsa-mir-153 x x hsa-mir-1537 x x xhsa-mir-193a- x 3p hsa-mir-219-5p x hsa-mir-29b x x hsa-mir-301a x xhsa-mir-32 x x x hsa-mir-33a x x x x hsa-mir-545 x hsa-mir-582-3p xhsa-mir-590-5p x x x hsa-mir-1181 x x hsa-mir-513a- x x 5p hsa-mir-132 xx hsa-mir-1296 x

Several groups of sequences have been discovered by use of the inventionwherein the sequences are identified with groups dichotomous withrespect to their response to an immunotherapy. Paired sequences, thosethat represent levels or differences in levels of one or moreimmune-cell comprised microRNA may be used clinically.

Accordingly, one embodiment includes the method of collecting samples ofimmune cells, preferably PBMCs, from a statistically sufficient numberof patients contemplating an immunotherapy before therapy and followingtherapy. It is understood that while PBMCs may be used, subsets of PBMCsmay be selected by means known in the art, for example flow cytometricsorting. Patients are sorted into dichotomous groups by the differences.Expected response ranges or normal ranges for patients in each group areestablished in accordance with practices well-known in the clinicallaboratory field. Patients may be classified as members of one or theother group wherein their individual results fall within non-overlappingranges by one or more microRNAs demonstrated to have the power todistinguish a patient population into the groups as described above.

In another embodiment, a panel of microRNA sequences of non-sequentialmicroRNAs quantified on immune-cell samples preferably PBMCs, takenprior to a contemplated immunotherapy may be used clinically tocatagorize patients into dichotomous groups. Samples of immune cells,preferably PBMCs, from a statistically sufficient number of patientscontemplating an immunotherapy are collected before therapy. Patientsare sorted into dichotomous groups by the differences. Expected responseranges or normal ranges for patients in each group are established inaccordance with practices well-known in the clinical laboratory field.Patients may be classified as members of one or the other group whereintheir individual results fall within non-overlapping ranges by one ormore microRNA. Said classification permits determining suitability ofthe contemplated immunotherapy, for example resistance to thecontemplated immunotherapy.

The invention not only teaches a method for the dichotomization ormultimerization of groups of clinically similar patients into groupsdistinguishable by their microRNA profiles, it also discloses importantmicroRNA species that are different within the groups that may be ofgreat clinical significance. For example, in a genome-wide dissection ofmicroRNA functions predicted by a computer algorithm, a number ofmicroRNAs were identified whose response patterns segregate withdichotomization were ascribed in vivo functions (John S. Tsang, MargaretS. Ebert, and Alexander van Oudenaarden, Genome-wide dissection ofmicroRNA functions and co-targeting networks using gene-set signatures,Mol Cell. 2010 Apr. 9; 38(1): 140-153. doi:10.1016/j.molce1.2010.03.007). Amongst microRNAs that showed significantdifferences in response between the two groups, hsa-mir-582-3p and140-5p was identified as differentially expressed in non-functioningpituitary adenomas (Pituitary (2011) 14:112-124). These microRNAs werepredicted to target Smad3, a member of the TGFβ signaling cascade. Itdoes not escape our attention that TGFβ is of primary importance inmaintenance of pregnancy. Onouchi and Hata identify IVIG resistance inapproximately fifteen percent of patients treated for Kawasaki Diseaserequiring increased dosing or alternative therapy. They associatedgenetic variations (SNPs) in the genes coding for ITPKC and caspase-3with IVIG unresponsiveness (Yoshihiro Onouchi and Akira Hata,Responsible Genetic Factors for Vasculitis in Kawasaki Disease, Advancesin the Etiology, Pathogenesis and Pathology of Vasculitis, 71-92).Identification of molecular markers that assess drug resistance orresponsiveness is very important clinically. Further it does not escapeus identification of patients into dichotomous groupings might allowdefinition of individuals with higher risk of diseases including but notlimited to autoimmune disease is an important aspect of this invention.

In a preferred embodiment the method comprises providing a biologicalsample comprising immune cells from a subject with a history ofreproductive disorder or risk of reproductive disorder said sample beingderived from immune cells, for example, derived from peripheral blood,and then isolating mononuclear cells as taught by Boyum (Boyum A 1983.Isolation of human blood monocytes with Nycodenz, a new non-ioniciodinated gradient medium. Scand J Immunol 17: 429-436). A kit optimizedfor recovery of microRNA sequences such as mirNeasy Mini Kit Qiagencatalogue 217004 may be used following instructions provided.Quantification of microRNA may be determined by a variety of techniquesknown to those skilled in the art. In a preferred embodiment, individualmicroRNAs are quantified by real-time polymerase chain reaction (PCR).In a more preferred embodiment, instructions provided for real-time,quantitative PCR are followed as provided by SABiosciences, Frederick,Md. for use with primers listed below and reagents optimized forspecific real-time thermocycling equipment such as the StratageneMx3005p (www.SABioscies.com). Operating instructions for the StratageneMx3005p are provided by the manufacturer. Comprised therein areinstructions for spectrophotometric quantification of recovered RNA,recommendations for input quantity of RNA and PCR master mix.Quantification may be performed concurrently with quantification of a“housekeeper gene” (a gene that is expressed with relative constancy inthe cells being interrogated thereby permitting relative quantification,preferably 18s RNA) and then determining the amount of microRNAs andcomparing to the amount of the corresponding microRNA in the sample tosimilarly treated biological sample from control individuals. Thesubject is then diagnosed as being a candidate for immunotherapy such asIVIG, lymphocyte immunotherapy or anti-TNF alpha therapy after themethods of Winger et. al. if there is differential expression and/orpattern of expression in the amount of one or more of the microRNAs fromthe sample as compared to corresponding microRNA control levels and/orcontrol patterns. Differential expression is defined statistically fromthree or more samples from control individuals wherein the patient valueand/or pattern is two standard deviations above the control mean valueand/or pattern. Preferably control values/patterns and patientvalues/patterns are determined on specimens drawn at the same time withrespect to pregnancy, for example, during the period of preconceptionand at the same time during a menstrual cycle or following implantation.In addition, the method can comprise quantification of a plurality ofsamples wherein the control sample is the first sample value for one ormore of the above listed microRNAs. Progress of the condition can beassessed by comparing values and/or patterns subsequent to the controlvalue and/or pattern. Efficacy of therapeutic intervention can bedetermined by comparing values and/or patterns subsequent to initiationof a therapeutic intervention to a control value and/or pattern on abiologic sample taken prior to initiation of therapy.

Other embodiments are directed to the diagnosis and treatment ofimmunological reproductive problems. According to the methods describedabove, PMBC MicroRNA levels and patterns may be used to diagnoseimmunological reproductive problems. Examples of such reproductiveproblems include implantation failure, infertility, miscarriage, pretermlabor, PROM (premature rupture of membranes), IUGR (intrauterine growthretardation), antiphospholipid antibody syndrome, stillbirth,endometriosis and others. Further, PMBC MicroRNA levels and patterns mayalso be used to monitor treatment for these immunological reproductiveproblems.

As will be appreciated, PBMC MicroRNA levels and patterns may also beused to diagnose and monitor treatment for reproductive problems thatmay not be immunological but may be correlated with immunologicalreproductive problems. Representative conditions include increasedtissue factor levels (microRNA 19b and 20a specifically) inAnti-phospholipid Antibody Syndrome, increased coagulation risk factors,PCOS (polycystic ovary syndrome) and premature ovarian failure (POF.)

In another aspect, PBMC MicroRNA levels and patterns may be used todiagnose and monitor treatment to prevent long term risks to the babyresulting from immunologically compromised pregnancy. These may includerisk of the baby developing asthma, autism, ADHD, diabetes,schizophrenia, Tourette' syndrome, bipolar disorder or other conditions.Further, PBMC MicroRNA levels and patterns may be used to diagnose andmonitor treatment for problems that may not be reproductive but may becorrelated with reproductive immunological-problems, includingautoimmune thyroiditis, migraines, lupus (SLE), rheumatoid arthritisflares, estrogen deficiency, osteoporosis, insulin resistance andothers.

The methods and compositions of the invention may also be applied to thediagnosis or treatment of non-reproductive immunological diseases.Specifically, PBMC MicroRNA levels and patterns may be used to diagnosepatients with non-reproductive immunological diseases that may that maybenefit from immunotherapy (such as IVIG), including Kawasaki disease,ITP, Guillain-Barre Syndrome, autism, MS, lupus (SLE) or otherconditions not yet identified that respond to immunotherapy treatment.

By identifying representative patient groups and microRNA responses,optimal immunologic and other therapies may be selected. Suitablerepresentative therapies include use of IVIG, intralipid, G-CSF(Neupogen), corticosteroids (Prednisone, prednisolone, dexamethasone,etc), anti-TNFα alpha therapies (Humira, Enbrel, Simponi), fish oil(omega-3 oils), vitamin D, lymphocyte immunization therapy (LIT),levothyroxin, metformin, heparin and others.

Further embodiments include using PBMC MicroRNA levels and patterns todetermine optimal doses and treatment choices and/or combinations tocontrol the specific immunologic conditions identified. Doses andcombinations may include a single 25g of IVIG dose combined with Clexane20 mg qd followed by microRNA retesting 2 weeks later, monthly Humirainjections followed by no retesting, monthly Intralipid only, 2 mgdexamethasone daily for 3 months then drug tapering, or others.

As will be appreciated, PBMC MicroRNA levels and patterns may be used toidentify patients that will not benefit or may experience negative sideeffects from therapy despite a positive disease diagnosis.

Yet another aspect of this disclosure is directed to identification ofcharacteristic behavioral levels and patterns of a PBMC MicroRNArelative to other MicroRNAs. These levels and patterns may be associatedwith different disease and treatment phenotypes. These may be defined byranges at which individual microRNAs operate, including basal levels,fully active levels, mean levels, observed standard deviations, frompatterns observed in individual levels, from sequential microRNA samplesbased on patterns of deltas (differences) between sequential samples,and patterns and pattern changes observed between multiple microRNAsfrom individual or sequential samples.

PBMC MicroRNA levels and patterns may also be defined in part bypolymorphisms. As discussed above, polymorphisms may occur in the 22-24base of the microRNA itself, in the region around the microRNA in thepre-mircroRNA that affect folding of the hairpin and its transcription,in the target mRNA, or in the primary RNA strand complementary to theguide strand entering the RISC complex, for example.

Some microRNa levels and patterns may be best measured by effectivelytesting microRNA changes within the mononuclear cell population via thePBMCs. Effectively testing microRNA mononuclear cell population isdemonstrated when removal of PBMCs eliminates the mononuclear microRNAmeasurement.

As discussed above, microRNA patterns and levels may be determined in anumber of suitable manners. In a preferred embodiment, bimodal(dichotomous) or multimodal behavior of one or more microRNAa may beverified in before and/or after treatment groups. Further, usefulclinical characteristics associated with these separated patient groups(such as degree of clinical improvement with IVIG), may be identified.Similarly, bimodal or multimodal behavior microRNAs may be used asmarkers to select optimal groups for further microRNA analysis or otherdiagnostic procedures. Also preferably, suitable methods includeselection of microRNAs in common between most increased and mostdecreased top fractions of separated groups refines the identificationof the groups.

Described herein are presently preferred embodiments. However, oneskilled in the art that pertains to the present invention willunderstand that the principles of this disclosure can be extended easilywith appropriate modifications to other applications.

1. A method for identifying at least two characteristic groups in apatient population on the basis of microRNA expression comprising thesteps of: a) collecting immune cells; b) extracting microRNA-comprisingRNA from the immune cells; c) quantifying at least one microRNA withinthe extracted RNA; and d) segregating the patient population into thegroups on the basis of expression of the at least one microRNA.
 2. Themethod of claim 1, wherein the step of collecting immune cells comprisescollecting peripheral blood mononuclear cells.
 3. The method of claim 1,wherein the step of segregating the patient population comprisesassigning patients expressing a relatively high level of the at leastone microRNA to a first group and assigning patients expressing arelatively low level of the at least one microRNA to a second group. 4.The method of claim 1, wherein the step of collecting immune cellscomprises collecting the cells before an immunotherapy treatment.
 5. Themethod of claim 1, wherein the step of collecting immune cells comprisescollecting the cells after an immunotherapy treatment.
 6. The method ofclaim 1, wherein the step of collecting immune cells comprisescollecting the cells before and after an immunotherapy treatment andwherein the step of segregating the patient population includesdetermining the change in expression level of the at least one microRNAafter the immunotherapy treatment.
 7. The method of claim 6, wherein thestep of segregating the patient population comprises assigning patientsexhibiting a first change in the expression level to a first group andassigning patients exhibiting a second change in the expression level toa second group.
 8. The method of claim 7, wherein the first change is arelatively large change in expression level and the second change is arelatively small change in expression level.
 9. The method of claim 7,wherein the first change is a positive change in expression level andthe second change is a negative change in expression level.
 10. Themethod of claim 7, wherein the absolute value of the mean of the changein the expression level of the at least one microRNA in the first groupdivided by the standard deviation is greater than or equal to one. 11.The method of claim 7, further comprising the step identifying a subsetof microRNAs within the group of known microRNAs that exhibit a changein expression level in the first group such that the absolute value ofthe mean of the change in expression level divided by the standarddeviation is greater than or equal to one.
 12. The method of claim 11,further comprising the step of identifying a microRNA within the groupof known microRNAs that exhibits the greatest change in expression levelin the first group.
 13. The method of claim 1, further comprising thesteps of: a) collecting immune cells from an additional patient; b)extracting microRNA-comprising RNA from the immune cells of theadditional patient; c) quantifying at least one microRNA within theextracted RNA from the additional patient; and d) identifying theadditional patient as belonging to one of the segregated groups on thebasis of expression of the at least one microRNA.
 14. The method ofclaim 13, further comprising the step of administering a treatment tothe additional patient based on the identification.
 15. The method ofclaim 14, wherein the step of administering a treatment comprisesadministering IVIG.
 16. The method of claim 1, further comprising thestep of diagnosing a patient as having a condition based on membershipin a segregated group.
 17. The method of claim 16, wherein the conditionis a reproductive disorder.
 18. The method of claim 1, furthercomprising the step of monitoring treatment of a patient belonging toone of the segregated groups by collecting immune cells, extracting atleast one microRNA and quantifying the at least one microRNA at asubsequent time.
 19. The method of claim 1, wherein the at least onemicroRNA is selected from the group consisting of hsa-let-7e,hsa-mir-1181, hsa-miR-1183, hsa-miR-1224-5p, hsa-miR-127-3p,hsa-mir-1296, hsa-mir-132, hsa-mir-136, hsa-miR-139-3p, hsa-mir-141,hsa-miR-142-3p, hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537,hsa-miR-154, hsa-miR-191, hsa-mir-193a-3p, hsa-miR-19a, hsa-mir-219-5p,hsa-mir-29b, hsa-mir-301a, hsa-miR-301b, hsa-miR-30e, hsa-mir-32,hsa-mir-33a, hsa-miR-340, hsa-miR-362-3p, hsa-miR-371-5p, has-377,hsa-miR-423-3p, hsa-miR-432, hsa-mir-513a-5p, hsa-mir-545,hsa-miR-548a-5p, hsa-miR-574-5p, hsa-mir-582-3p, hsa-mir-590-5p,hsa-mir-15a, hsa-mir-548c-5p, hsa-mir-1225-3p, hsa-mir-29b, hsa-mir-21,hsa-mir-1237, hsa-mir-101, hsa-mir-1539, hsa-mir-557, hsa-mir-125a-3pand hsa-mir-423-5p.
 20. The method of claim 19, wherein the at least onemicroRNA is selected from the group consisting of hsa-mir-136,hsa-mir-141, hsa-mir-142-5p, hsa-mir-144, hsa-mir-153, hsa-mir-1537,hsa-mir-193a-3p, hsa-mir-219-5p, hsa-mir-29b, hsa-mir-301a, hsa-mir-32,hsa-mir-33a, hsa-mir-545, hsa-mir-582-3p, hsa-mir-590-5p, hsa-mir-1181,hsa-mir-513a-5p, hsa-mir-132 and hsa-mir-1296.